262 lines
8.9 KiB
Python
262 lines
8.9 KiB
Python
"""E2E tests for streaming dataset functionality"""
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import pytest
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from .utils import check_model_output_exists, check_tensorboard
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class TestStreamingDatasets:
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"""Test case for streaming datasets with different mixing strategies"""
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@pytest.mark.parametrize(
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("dataset_mixing_strategy", "mixing_weights"),
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[
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("round_robin", None),
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("weighted", [0.7, 0.3]),
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("random", None),
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],
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)
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def test_streaming_dataset_mixing_strategies(
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self, temp_dir, dataset_mixing_strategy, mixing_weights
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):
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"""Test different mixing strategies with streaming datasets"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"flash_attention": True,
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"sequence_len": 1024,
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"sample_packing": False,
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"dataset_processes": 1,
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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": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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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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# Streaming config
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"streaming": True,
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"max_steps": 3, # Very small for smoke test
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"dataset_mixing_strategy": dataset_mixing_strategy,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"val_set_size": 0.0,
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"output_dir": temp_dir,
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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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"save_safetensors": True,
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"bf16": "auto",
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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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# Add mixing weights if specified
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if mixing_weights:
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cfg["mixing_weights"] = mixing_weights
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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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# Verify training actually happened by checking loss decrease
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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2.5, # Loss should be reasonable for a smoke test (higher threshold for streaming)
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"Train Loss (%s) is too high",
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)
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def test_streaming_eval_specific_mixing(self, temp_dir):
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"""Test eval-specific mixing strategy override"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"flash_attention": True,
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"sequence_len": 512,
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"sample_packing": False,
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"dataset_processes": 1,
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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": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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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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"test_datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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"split": "train", # Specify train split for eval dataset
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},
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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"split": "train", # Specify train split for eval dataset
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},
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],
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# Streaming config
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"streaming": True,
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"eval_streaming": True,
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"max_steps": 3,
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# Different mixing for train vs eval
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"dataset_mixing_strategy": "round_robin",
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"eval_dataset_mixing_strategy": "weighted",
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"eval_mixing_weights": [0.6, 0.4],
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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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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"save_safetensors": True,
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"bf16": "auto",
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"use_tensorboard": True,
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"save_first_step": False,
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"eval_steps": 3, # Eval at the end
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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# Check both train and eval losses
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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2.5,
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"Train Loss (%s) is too high",
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)
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check_tensorboard(
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temp_dir + "/runs",
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"eval/eval_loss",
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2.5,
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"Eval Loss (%s) is too high",
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)
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def test_streaming_validation_error(self, temp_dir):
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"""Test that pydantic validation catches invalid streaming configs"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"streaming": True,
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"max_steps": 3,
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# Invalid: wrong number of weights for datasets
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"dataset_mixing_strategy": "weighted",
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"mixing_weights": [1.0], # Should be [0.x, 0.y] for 2 datasets
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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}
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)
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# This should raise a validation error
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with pytest.raises(Exception) as exc_info:
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validate_config(cfg)
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# Verify it's the right validation error
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assert "mixing_weights length" in str(exc_info.value)
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assert "must match number of datasets" in str(exc_info.value)
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def test_streaming_three_datasets_weighted(self, temp_dir):
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"""Test weighted mixing with three datasets"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"flash_attention": True,
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"sequence_len": 512,
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"sample_packing": False,
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"dataset_processes": 1,
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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": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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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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"path": "yahma/alpaca-cleaned",
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"type": "alpaca",
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},
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],
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# Streaming config
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"streaming": True,
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"max_steps": 3,
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"dataset_mixing_strategy": "weighted",
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"mixing_weights": [0.5, 0.3, 0.2],
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"val_set_size": 0.0,
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"output_dir": temp_dir,
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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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"save_safetensors": True,
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"bf16": "auto",
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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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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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2.5,
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"Train Loss (%s) is too high",
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)
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