fc4e37920b
* Prepare for transformers v5 upgrade * fix hf cli * update for hf hub changes * fix tokenizer apply_chat_template args * remap include_tokens_per_second * fix tps * handle migration for warmup * use latest hf hub * Fix scan -> ls * fix import * fix for renaming of mistral common tokenizer -> backend * update for fixed tokenziation for llama * Skip phi35 tests for now * remove mistral patch fixed upstream in huggingface/transformers#41439 * use namespacing for patch * don't rely on sdist for e2e tests for now * run modal ci without waiting too * Fix dep for ci * fix imports * Fix fp8 check * fsdp2 fixes * fix version handling * update fsdp version tests for new v5 behavior * Fail multigpu tests after 3 failures * skip known v5 broken tests for now and cleanup * bump deps * unmark skipped test * re-enable test_fsdp_qlora_prequant_packed test * increase multigpu ci timeout * skip broken gemma3 test * reduce timout back to original 120min now that the hanging test is skipped * fix for un-necessary collator for pretraining with bsz=1 * fix: safe_serialization deprecated in transformers v5 rc01 (#3318) * torch_dtype deprecated * load model in float32 for consistency with tests * revert some test fixtures back * use hf cache ls instead of scan * don't strip fsdp_version more fdsp_Version fixes for v5 fix version in fsdp_config fix aliasing fix fsdp_version check check fsdp_version is 2 in both places * Transformers v5 rc2 (#3347) * bump dep * use latest fbgemm, grab model config as part of fixture, un-skip test * import AutoConfig * don't need more problematic autoconfig when specifying config.json manually * add fixtures for argilla ultrafeedback datasets * download phi4-reasoning * fix arg * update tests for phi fast tokenizer changes * use explicit model types for gemma3 --------- Co-authored-by: Wing Lian <wing@axolotl.ai> * fix: AutoModelForVision2Seq -> AutoModelForImageTextToText * chore: remove duplicate * fix: attempt fix gemma3 text mode * chore: lint * ga release of v5 * need property setter for name_or_path for mistral tokenizer * vllm not compatible with transformers v5 * setter for chat_template w mistral too --------- Co-authored-by: NanoCode012 <nano@axolotl.ai> Co-authored-by: salman <salman.mohammadi@outlook.com>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""
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E2E smoke tests for LLMCompressorPlugin integration
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"""
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from pathlib import Path
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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, 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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require_llmcompressor,
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require_torch_2_4_1,
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)
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MODELS = [
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"nm-testing/llama2.c-stories42M-pruned2.4-compressed",
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"nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
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]
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@pytest.mark.parametrize(
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"base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
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)
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@pytest.mark.parametrize(
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"save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
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)
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class TestLLMCompressorIntegration:
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"""
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e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
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"""
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@require_llmcompressor
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@require_torch_2_4_1
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def test_llmcompressor_plugin(
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self, temp_dir, base_model: str, save_compressed: bool
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):
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from llmcompressor import active_session
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# core cfg
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cfg = DictDefault(
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{
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"base_model": base_model,
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"plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
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"sequence_len": 1024,
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"val_set_size": 0.05,
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"special_tokens": {"pad_token": "<|endoftext|>"},
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"datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
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"num_epochs": 1,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 1e-5,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"bf16": "auto",
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"max_steps": 5,
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"llmcompressor": {
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"recipe": {
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"finetuning_stage": {
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"finetuning_modifiers": {
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"ConstantPruningModifier": {
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"targets": [
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"re:.*q_proj.weight",
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"re:.*k_proj.weight",
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"re:.*v_proj.weight",
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"re:.*o_proj.weight",
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"re:.*gate_proj.weight",
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"re:.*up_proj.weight",
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"re:.*down_proj.weight",
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],
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"start": 0,
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},
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},
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},
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},
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"save_compressed": save_compressed,
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},
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"save_first_step": False,
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}
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)
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prepare_plugins(cfg)
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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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try:
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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_llmcompressor_model_outputs(temp_dir, save_compressed)
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finally:
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active_session().reset()
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def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
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if save_compressed:
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assert (Path(temp_dir) / "recipe.yaml").exists()
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from compressed_tensors import ModelCompressor
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from compressed_tensors.config import Sparse24BitMaskConfig
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compressor = ModelCompressor.from_pretrained(temp_dir)
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assert compressor is not None
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assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)
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