168 lines
5.1 KiB
Python
168 lines
5.1 KiB
Python
"""
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Simple end-to-end test for Cut Cross Entropy integration
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"""
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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 import get_pytorch_version
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from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import (
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check_model_output_exists,
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check_tensorboard_loss_decreased,
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)
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@pytest.fixture()
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def min_cfg(temp_dir):
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return {
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"plugins": [
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"axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin",
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],
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"cut_cross_entropy": True,
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"sequence_len": 1024,
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"val_set_size": 0.02,
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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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"num_epochs": 1,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"learning_rate": 5e-4,
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"optimizer": "adamw_torch_fused",
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"output_dir": temp_dir,
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"lr_scheduler": "cosine",
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"max_steps": 40,
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"warmup_steps": 5,
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"bf16": "auto",
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"save_first_step": False,
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"use_tensorboard": True,
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"seed": 42,
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}
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class TestCutCrossEntropyIntegration:
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"""
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e2e tests for cut_cross_entropy integration with Axolotl
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"""
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def test_llama_w_cce(self, min_cfg, temp_dir):
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cfg = DictDefault(min_cfg)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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major, minor, _ = get_pytorch_version()
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if (major, minor) < (2, 4):
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with pytest.raises(ImportError):
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train(cfg=cfg, dataset_meta=dataset_meta)
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else:
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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_loss_decreased(
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temp_dir + "/runs",
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initial_window=5,
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final_window=5,
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max_initial=2.2,
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max_final=2.0,
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)
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def test_qwen2_w_cce(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "axolotl-ai-co/tiny-qwen2-129m",
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"plugins": [
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"axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin",
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],
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"cut_cross_entropy": True,
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"sequence_len": 1024,
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"val_set_size": 0.02,
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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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"num_epochs": 1,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 1,
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"learning_rate": 2e-4,
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"optimizer": "adamw_torch_fused",
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"output_dir": temp_dir,
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"lr_scheduler": "cosine",
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"max_steps": 50,
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"bf16": "auto",
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"save_first_step": False,
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"use_tensorboard": True,
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"seed": 42,
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}
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)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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major, minor, _ = get_pytorch_version()
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if (major, minor) < (2, 4):
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with pytest.raises(ImportError):
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train(cfg=cfg, dataset_meta=dataset_meta)
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else:
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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_loss_decreased(
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temp_dir + "/runs",
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initial_window=5,
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final_window=5,
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max_initial=5.0,
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max_final=4.7,
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)
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@pytest.mark.parametrize(
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"attention_type",
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[
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"flash_attention",
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"sdp_attention",
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# "xformers_attention",
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],
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)
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def test_llama_w_cce_and_attention(self, min_cfg, temp_dir, attention_type):
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cfg = DictDefault(
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min_cfg
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| {
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attention_type: True,
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}
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)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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major, minor, _ = get_pytorch_version()
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if (major, minor) < (2, 4):
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with pytest.raises(ImportError):
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train(cfg=cfg, dataset_meta=dataset_meta)
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else:
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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_loss_decreased(
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temp_dir + "/runs",
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initial_window=5,
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final_window=5,
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max_initial=2.2,
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max_final=2.0,
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)
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