c67910fa6f
* bump hf deps * upgrade liger-kernel too * install cce from fork for transformers fix * fix reference to vocab size in gemma3 patch * use padding_idx instead of pad_token_id * remove fixed gemma3 patch * use updated cce fork * fix local mllama cce patches w docstring * add test for multipack with trainer setup and fix trainer for trainer refactor upstream * bump modal version * guard for iterable datasetS * mllama model arch layout changed in latest transformers * fix batch sampler with drop_last * fix: address upstream vlm changes for lora * fix: update references to old lora target path * fix: remove mllama fa2 patch due to upstream fix * fix: lora kernel patch path for multimodal models * fix: removed mllama from quarto * run test for came optim on 2.6.0+ * fix fsdp2 patch and remove deprecated patch * make sure to set sequence_parallel_degree for grpo * Add SP test for GRPO * add sp to grpo config for trainer * use reward_funcs as kwarg to grpo trainer * fix the comprehension for reward funcs * reward funcs already passed in as args * init sp_group right before training * fix check for adding models to SP context * make sure to pass args to super * upgrade deepspeed * use updated trl and add reasoning flags for vllm * patch the worker --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
126 lines
4.0 KiB
Python
126 lines
4.0 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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from axolotl.cli.args import TrainerCliArgs
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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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@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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"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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"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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"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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},
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],
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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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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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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.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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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, dataset_meta=dataset_meta)
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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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