Add: Sparse Finetuning Integration with llmcompressor (#2479)

* Add: SFTPlugin with llmcompressor * Update: review comments! * Add:llmcompressor instalable * pre commit hooks * Use: warning over warn * Revert: TODO's * Update llmcompressor version to latest * Apply suggestions from @markurtz Co-authored-by: Mark Kurtz <mark.j.kurtz@gmail.com> * Address review comments from @markurtz * Add: llcompressor installable * Rename: sft.yaml to sparse-finetuning.yaml * Use: absolute import * Update model config * Move: LLMCompressorPlugin into it's own submodule * Add: `llm_compressor` integration documentation * Rebase and updates! * Tests, Style, Updates * Add: .qmd file * Address Review Comments: * deleted redundant docs/llm_compressor.qmd * incorporated feedback in integration README.md * added llmcompressor integration to docs/custom_integrations.qmd Signed-off-by: Rahul Tuli <rtuli@redhat.com> * Add: line about further optimizations using llmcompressor Signed-off-by: Rahul Tuli <rtuli@redhat.com> * Apply patch from @winglian Signed-off-by: Rahul Tuli <rtuli@redhat.com> * Fix: Test Signed-off-by: Rahul Tuli <rtuli@redhat.com> * additional fixes for docker and saving compressed * split llmcompressor from vllm checks * Reset session between tests Signed-off-by: Rahul Tuli <rtuli@redhat.com> * move decorator to test method instead of class * make sure to reset the session after each test * move import of llmcompressor to reset session inside test --------- Signed-off-by: Rahul Tuli <rtuli@redhat.com> Co-authored-by: Mark Kurtz <mark.j.kurtz@gmail.com> Co-authored-by: Wing Lian <wing@axolotl.ai>
2025-05-01 11:25:16 -05:00
parent e963990ad7
commit 996fc124e5
13 changed files with 619 additions and 2 deletions
--- a/tests/e2e/integrations/test_llm_compressor.py
+++ b/tests/e2e/integrations/test_llm_compressor.py
@@ -0,0 +1,111 @@
+"""
+E2E smoke tests for LLMCompressorPlugin integration
+"""
+
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import (
+    check_model_output_exists,
+    require_llmcompressor,
+    require_torch_2_4_1,
+)
+
+MODELS = [
+    "nm-testing/llama2.c-stories42M-pruned2.4-compressed",
+    "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
+]
+
+
+@pytest.mark.parametrize(
+    "base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
+)
+@pytest.mark.parametrize(
+    "save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
+)
+class TestLLMCompressorIntegration:
+    """
+    e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
+    """
+
+    @require_llmcompressor
+    @require_torch_2_4_1
+    def test_llmcompressor_plugin(
+        self, temp_dir, base_model: str, save_compressed: bool
+    ):
+        from llmcompressor import active_session
+
+        # core cfg
+        cfg = DictDefault(
+            {
+                "base_model": base_model,
+                "plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
+                "sequence_len": 1024,
+                "val_set_size": 0.05,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 1e-5,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "max_steps": 5,
+                "llmcompressor": {
+                    "recipe": {
+                        "finetuning_stage": {
+                            "finetuning_modifiers": {
+                                "ConstantPruningModifier": {
+                                    "targets": [
+                                        "re:.*q_proj.weight",
+                                        "re:.*k_proj.weight",
+                                        "re:.*v_proj.weight",
+                                        "re:.*o_proj.weight",
+                                        "re:.*gate_proj.weight",
+                                        "re:.*up_proj.weight",
+                                        "re:.*down_proj.weight",
+                                    ],
+                                    "start": 0,
+                                },
+                            },
+                        },
+                    },
+                    "save_compressed": save_compressed,
+                },
+            }
+        )
+
+        prepare_plugins(cfg)
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        try:
+            train(cfg=cfg, dataset_meta=dataset_meta)
+            check_model_output_exists(temp_dir, cfg)
+            _check_llmcompressor_model_outputs(temp_dir, save_compressed)
+        finally:
+            active_session().reset()
+
+
+def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
+    if save_compressed:
+        assert (Path(temp_dir) / "recipe.yaml").exists()
+
+        from compressed_tensors import ModelCompressor
+        from compressed_tensors.config import Sparse24BitMaskConfig
+
+        compressor = ModelCompressor.from_pretrained(temp_dir)
+        assert compressor is not None
+        assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)