KD fix w/ online distillation (#2700) [skip ci]
* 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
This commit is contained in:
Wing Lian
GitHub
@@ -5,10 +5,9 @@ e2e tests for kd trainer support in Axolotl
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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.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, prepare_plugins, validate_config
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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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@@ -17,8 +16,8 @@ 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": "osllmai-community/Llama-3.2-1B",
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"tokenizer_config": "axolotl-ai-co/Llama-3.3-70B-Instruct-tokenizer",
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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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@@ -31,20 +30,22 @@ def min_cfg(temp_dir):
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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": "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample",
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"type": "axolotl.integrations.kd.chat_template",
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"field_messages": "messages_combined",
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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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"logprobs_field": "llm_text_generation_vllm_logprobs",
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"temperature": 1.0,
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"preprocess_shards": 2,
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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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@@ -80,17 +81,29 @@ class TestKnowledgeDistillation:
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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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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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# 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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train(cfg=cfg, dataset_meta=dataset_meta)
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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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@@ -110,12 +123,22 @@ class TestKnowledgeDistillation:
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| kd_min_cfg
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)
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# pylint: disable=duplicate-code
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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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# 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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train(cfg=cfg, dataset_meta=dataset_meta)
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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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