ccc94da8ad
* kd fixes * fix collator setup * fix input args * better handling to drop string fields for kd with raw dataset * kd trainer has kd temp as part of the init * drop top_k before softmax * simplfy and remove zscore * WIP chunked KD loss with autograd wrapper * more fixes and liger-type chunked loss * collator cls for plugins * remove debugging * additional plugin collator kwargs, don't scale up kd loss by t^2 * don't need temp arg to distill method * online kd wip * add close to comment block * suport sampling params/max new tokens * handle when no custom collator is used in plugins * logsumexp trick: * fix check * shift off the first empty token * fix length of padding * use max not min * temp scale kd loss at end * support for dynamic plugin training args mixins and symmetric kl * chore: lint * fix trainer callback base class * Fix decay * accept compressed responses for smaller wire payload * post-rebase lint * more KD updates * increase hyperparams_count for gradients for added normalize_topk * fix to remove attention_mask * rename vars for consistency * fix rebase issues * default to dropping last batch in multipack batch sampler * improve handling of train len * init collator_cls_and_kwargs * explicit drop_last=False when checking for multipack completeness * use separate v2 loader for kd * fix kd tests to use subprocess so it picks up kd training args * default value for kd_beta arg * use updated dataset for ci * longer timeout for e2e
146 lines
4.6 KiB
Python
146 lines
4.6 KiB
Python
"""
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e2e tests for kd trainer support in Axolotl
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"""
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from pathlib import Path
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import pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async, 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_5_1
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@pytest.fixture(name="kd_min_cfg")
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def min_cfg(temp_dir):
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return {
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"base_model": "Qwen/Qwen3-0.6B",
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"tokenizer_config": "winglian/qwen3-14b-math",
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"plugins": [
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"axolotl.integrations.kd.KDPlugin",
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"axolotl.integrations.liger.LigerPlugin",
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],
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"liger_rms_norm": True,
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"liger_glu_activation": True,
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"torch_compile": True,
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"chat_template": "llama3",
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"kd_trainer": True,
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"kd_ce_alpha": 0.1,
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"kd_alpha": 0.9,
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"kd_temperature": 1.0,
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"kd_beta": 0.0,
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"kd_normalize_topk": True,
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"dataloader_prefetch_factor": 8,
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"dataloader_num_workers": 4,
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"dataloader_pin_memory": True,
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"datasets": [
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{
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"path": "winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-topk128-normalized",
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"type": "chat_template",
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"split": "train",
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"split_thinking": True,
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"eot_tokens": ["<|im_end|>"],
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"data_files": ["train/batch-000000.parquet"],
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},
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],
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"skip_prepare_dataset": True,
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"val_set_size": 0.0,
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"sequence_len": 2048,
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"sample_packing": True,
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"pad_to_sequence_len": True,
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"gradient_accumulation_steps": 2,
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"micro_batch_size": 1,
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"num_epochs": 1,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"learning_rate": 0.00001,
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"bf16": "auto",
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"gradient_checkpointing": True,
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"flash_attention": True,
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"special_tokens": {
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"pad_token": "<|end_of_text|>",
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"eos_token": "<|eot_id|>",
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},
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"max_steps": 5,
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"output_dir": temp_dir,
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"save_safetensors": True,
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"use_tensorboard": True,
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}
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class TestKnowledgeDistillation:
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"""
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Test case for Knowledge Distillation
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"""
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# While this will run on torch 2.4.x without torch_compile enabled
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# the VRAM requirement is higher than what is available in CI
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@require_torch_2_5_1
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def test_llama_kd(self, temp_dir, kd_min_cfg):
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cfg = DictDefault(kd_min_cfg)
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# pylint: disable=duplicate-code
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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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"1",
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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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assert (Path(temp_dir) / "model.safetensors").exists()
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check_tensorboard(
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temp_dir + "/runs", "train/loss", 1.4, "Train Loss (%s) is too high"
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)
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@pytest.mark.skip(reason="Chunked KD loss doesn't support PEFT/LoRA")
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@pytest.mark.parametrize(
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"load_in_8bit",
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[True, False],
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)
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def test_llama_lora_kd(self, temp_dir, kd_min_cfg, load_in_8bit):
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cfg = DictDefault(
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{
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"load_in_8bit": load_in_8bit,
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"torch_compile": False,
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"adapter": "lora",
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"peft_use_dora": True,
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"lora_target_linear": True,
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"lora_r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.0,
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}
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| kd_min_cfg
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)
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# pylint: disable=duplicate-code
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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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"1",
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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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assert (Path(temp_dir) / "adapter_model.safetensors").exists()
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check_tensorboard(
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temp_dir + "/runs", "train/loss", 1.2, "Train Loss (%s) is too high"
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)
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