handle empty offset for quant state

* only configure logging on cli to play nicely with colab

* allow reloading the config on the fly from a dict

* make sure to use dict for yaml

* reuse existing function for load

* make cli args optional

* mps fix and respect max_steps

.github/workflows/main.yml

@@ -30,7 +30,7 @@ jobs:
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
-            axolotl_extras: vllm
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests.yml

@@ -261,6 +261,18 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras: llmcompressor
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.runpod/tests.json

@@ -0,0 +1,90 @@
+{
+  "tests": [
+    {
+      "name": "quick_smoke_test_sft",
+      "input": {
+        "user_id": "user",
+        "model_id": "llama-test",
+        "run_id": "llama-test",
+        "credentials": {
+          "wandb_api_key": "",
+          "hf_token": ""
+        },
+        "args": {
+          "base_model": "HuggingFaceTB/SmolLM2-135M",
+          "model_type": "AutoModelForCausalLM",
+          "tokenizer_type": "AutoTokenizer",
+          "load_in_4bit": true,
+          "strict": false,
+          "datasets": [
+            {
+              "path": "mhenrichsen/alpaca_2k_test",
+              "type": "alpaca",
+              "split": "train[:10%]"
+            }
+          ],
+          "val_set_size": 0.02,
+          "output_dir": "./outputs/lora-out",
+          "sequence_len": 4096,
+          "sample_packing": true,
+          "eval_sample_packing": false,
+          "pad_to_sequence_len": true,
+          "adapter": "qlora",
+          "lora_r": 32,
+          "lora_alpha": 64,
+          "lora_dropout": 0.05,
+          "lora_target_linear": true,
+          "lora_modules_to_save": [
+            "embed_tokens",
+            "lm_head"
+          ],
+          "gradient_accumulation_steps": 2,
+          "micro_batch_size": 1,
+          "num_epochs": 1,
+          "optimizer": "adamw_torch_fused",
+          "lr_scheduler": "cosine",
+          "learning_rate": 0.0002,
+          "train_on_inputs": false,
+          "group_by_length": false,
+          "bf16": "auto",
+          "tf32": true,
+          "gradient_checkpointing": true,
+          "logging_steps": 1,
+          "flash_attention": true,
+          "warmup_steps": 1,
+          "evals_per_epoch": 1,
+          "eval_max_new_tokens": 128,
+          "saves_per_epoch": 1,
+          "weight_decay": 0.0,
+          "special_tokens": {
+            "pad_token": "<|endoftext|>"
+          },
+          "max_steps": 20
+        }
+      },
+      "timeout": 100000
+    }
+  ],
+  "config": {
+    "gpuTypeId": "NVIDIA GeForce RTX 4090",
+    "gpuCount": 1,
+    "containerDiskInGb": 200,
+    "env": [
+      {
+        "key": "TOKENIZER",
+        "value": ""
+      },
+      {
+        "key": "DISABLE_LOG_STATS",
+        "value": "true"
+      }
+    ],
+    "allowedCudaVersions": [
+      "12.8",
+      "12.7",
+      "12.6",
+      "12.5",
+      "12.4"
+    ]
+  }
+}

docs/custom_integrations.qmd

@@ -49,7 +49,8 @@ sections = [
     ("Knowledge Distillation (KD)", "kd"),
     ("Liger Kernels", "liger"),
     ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
+    ("Spectrum", "spectrum"),
+    ("LLMCompressor", "llm_compressor")
 ]
 
 for section_name, folder_name in sections:

requirements.txt

@@ -18,7 +18,7 @@ accelerate==1.6.0
 datasets==3.5.0
 deepspeed>=0.15.4
 trl==0.17.0
-hf_xet==1.0.0
+hf_xet==1.1.0
 hqq==0.2.5
 
 optimum==1.16.2

src/axolotl/cli/__init__.py

@@ -2,4 +2,7 @@
 
 import os
 
+from axolotl.logging_config import configure_logging
+
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+configure_logging()

src/axolotl/cli/checks.py

@@ -8,9 +8,6 @@ from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 
-from axolotl.logging_config import configure_logging
-
-configure_logging()
 LOG = logging.getLogger(__name__)
 
 

src/axolotl/cli/config.py

@@ -5,6 +5,7 @@ import logging
 import os
 import tempfile
 from pathlib import Path
+from tempfile import NamedTemporaryFile
 from typing import Union
 from urllib.parse import urlparse
 
@@ -158,7 +159,9 @@ def plugin_set_cfg(cfg: DictDefault):
         plugin_manager.cfg = cfg
 
 
-def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefault:
+def load_cfg(
+    config: str | Path | DictDefault = Path("examples/"), **kwargs
+) -> DictDefault:
     """
     Loads the `axolotl` configuration stored at `config`, validates it, and performs
     various setup.
@@ -170,13 +173,24 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     Returns:
         `DictDefault` mapping configuration keys to values.
     """
-    config = check_remote_config(config)
+    if isinstance(config, (str, Path)):
-    if Path(config).is_dir():
+        config = check_remote_config(config)
-        config = choose_config(Path(config))
+        if Path(config).is_dir():
+            config = choose_config(Path(config))
 
-    # Load the config from the yaml file
+        # Load the config from the yaml file
-    with open(config, encoding="utf-8") as file:
+        with open(config, encoding="utf-8") as file:
-        cfg: DictDefault = DictDefault(yaml.safe_load(file))
+            cfg: DictDefault = DictDefault(yaml.safe_load(file))
+
+        cfg.axolotl_config_path = config
+    else:
+        cfg = config
+        with NamedTemporaryFile(
+            mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+        ) as temp_file:
+            temp_file.write(yaml.dump(config.to_dict()))
+            temp_file.close()
+        cfg.axolotl_config_path = temp_file.name
 
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
@@ -190,8 +204,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
             else:
                 cfg[k] = kwargs[k]
 
-    cfg.axolotl_config_path = config
-
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)

src/axolotl/cli/utils.py

@@ -20,11 +20,9 @@ from transformers import (
     ProcessorMixin,
 )
 
-from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 
-configure_logging()
 LOG = logging.getLogger(__name__)
 
 

src/axolotl/common/datasets.py

@@ -47,7 +47,7 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
 def load_datasets(
     *,
     cfg: DictDefault,
-    cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
+    cli_args: PreprocessCliArgs | TrainerCliArgs | None = None,
 ) -> TrainDatasetMeta:
     """
     Loads one or more training or evaluation datasets, calling
@@ -64,7 +64,8 @@ def load_datasets(
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
     preprocess_iterable = (
-        hasattr(cli_args, "iterable")
+        cli_args
+        and hasattr(cli_args, "iterable")
         and cli_args.iterable is not None
         and cli_args.iterable
     )
@@ -76,7 +77,7 @@ def load_datasets(
         preprocess_iterable=preprocess_iterable,
     )
 
-    if (
+    if cli_args and (
         cli_args.debug
         or cfg.debug
         or cli_args.debug_text_only

src/axolotl/core/trainer_builder.py

@@ -488,7 +488,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         # these are all the "standard" kwargs that are def used
         training_arguments_kwargs["max_steps"] = (
-            total_num_steps if self.cfg.max_steps else -1
+            self.cfg.max_steps if self.cfg.max_steps else -1
         )
         training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
         training_arguments_kwargs["per_device_train_batch_size"] = (

src/axolotl/core/trainers/grpo/__init__.py

@@ -63,6 +63,7 @@ class GRPOStrategy:
 
         grpo_args_kwargs["max_completion_length"] = trl.max_completion_length
         grpo_args_kwargs["log_completions"] = trl.log_completions
+        grpo_args_kwargs["num_completions_to_print"] = trl.num_completions_to_print
 
         if trl.reward_weights:
             grpo_args_kwargs["reward_weights"] = trl.reward_weights

src/axolotl/evaluate.py

@@ -11,7 +11,6 @@ from accelerate.logging import get_logger
 from datasets import Dataset
 from transformers.trainer import Trainer
 
-from axolotl.logging_config import configure_logging
 from axolotl.train import (
     TrainDatasetMeta,
     setup_model_and_tokenizer,
@@ -24,7 +23,6 @@ project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
-configure_logging()
 LOG = get_logger(__name__)
 
 

src/axolotl/integrations/llm_compressor/README.md

@@ -45,6 +45,7 @@ llmcompressor:
             're:.*down_proj.weight',
           ]
           start: 0
+  save_compressed: true
 # ... (other training arguments)
 ```
 
@@ -52,19 +53,56 @@ This plugin **does not apply pruning or sparsification itself** — it is intend
 
 Pre-sparsified checkpoints can be:
 - Generated using [LLMCompressor](https://github.com/vllm-project/llm-compressor)
-- Or downloaded from [Neural Magic's Hugging Face page](https://huggingface.co/neuralmagic)
+- Downloaded from [Neural Magic's Hugging Face page](https://huggingface.co/neuralmagic)
+- Any custom LLM with compatible sparsity patterns that you've created yourself
 
 To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:
 [https://github.com/vllm-project/llm-compressor/blob/main/README.md](https://github.com/vllm-project/llm-compressor/blob/main/README.md)
 
+### Storage Optimization with save_compressed
+
+Setting `save_compressed: true` in your configuration enables saving models in a compressed format, which:
+- Reduces disk space usage by approximately 40%
+- Maintains compatibility with vLLM for accelerated inference
+- Maintains compatibility with llmcompressor for further optimization (example: quantization)
+
+This option is highly recommended when working with sparse models to maximize the benefits of model compression.
+
 ### Example Config
 
 See [`examples/llama-3/sparse-finetuning.yaml`](examples/llama-3/sparse-finetuning.yaml) for a complete example.
 
 ---
 
+## Inference with vLLM
+
+After fine-tuning your sparse model, you can leverage vLLM for efficient inference.
+You can also use LLMCompressor to apply additional quantization to your fine-tuned
+sparse model before inference for even greater performance benefits.:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+llm = LLM("path/to/your/sparse/model")
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+For more details on vLLM's capabilities and advanced configuration options, see the [official vLLM documentation](https://docs.vllm.ai/).
+
 ## Learn More
 
 For details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:
 
-👉 [https://github.com/vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor)
+[https://github.com/vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor)

src/axolotl/kernels/quantize.py

@@ -55,13 +55,16 @@ def dequantize(
     target_device = W.device
 
     # Extract quantization state
+    nested = False
     if not isinstance(quant_state, list):
         # New style quant_state class
         absmax = quant_state.absmax.to(target_device)
         shape = quant_state.shape
         dtype = quant_state.dtype
         blocksize = quant_state.blocksize
-        offset = quant_state.offset.to(target_device)
+        if quant_state.nested:
+            nested = True
+            offset = quant_state.offset.to(target_device)
         state2 = quant_state.state2
         absmax2 = state2.absmax.to(target_device)
         code2 = state2.code.to(target_device)
@@ -115,7 +118,8 @@ def dequantize(
             ctypes.c_int(n_elements_absmax),
         )
 
-    out_absmax += offset
+    if nested:
+        out_absmax += offset
 
     # Choose appropriate dequantization function
     fx = (

src/axolotl/monkeypatch/attention/ring_attn/patch.py

@@ -12,10 +12,8 @@ import torch
 import torch.distributed as dist
 from accelerate.logging import get_logger
 
-from axolotl.logging_config import configure_logging
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 
-configure_logging()
 LOG = get_logger(__name__)
 
 

src/axolotl/train.py

@@ -30,7 +30,6 @@ from axolotl.core.trainers.mixins.sequence_parallel import (
     SequenceParallelContextManager,
 )
 from axolotl.integrations.base import PluginManager
-from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
@@ -42,7 +41,6 @@ try:
 except ImportError:
     BetterTransformer = None
 
-configure_logging()
 LOG = get_logger(__name__)
 
 
@@ -288,7 +286,19 @@ def save_trained_model(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
-    elif hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+    elif cfg.local_rank == 0:
+        if cfg.flash_optimum and BetterTransformer:
+            model = BetterTransformer.reverse(model)
+
+        if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
+            trainer.model.save_pretrained(
+                cfg.output_dir, safe_serialization=safe_serialization
+            )
+
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+
+    if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+        # TODO: add integration support so this can be implemented completely within the plugin
         from axolotl.integrations.llm_compressor.utils import (
             save_compressed_model,
         )
@@ -301,17 +311,6 @@ def save_trained_model(
             save_compressed=cfg.llmcompressor.save_compressed,
         )
 
-    elif cfg.local_rank == 0:
-        if cfg.flash_optimum and BetterTransformer:
-            model = BetterTransformer.reverse(model)
-
-        if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
-            trainer.model.save_pretrained(
-                cfg.output_dir, safe_serialization=safe_serialization
-            )
-
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-
 
 def create_model_card(cfg: DictDefault, trainer: Trainer):
     """

src/axolotl/utils/config/__init__.py

@@ -67,7 +67,7 @@ def resolve_dtype(cfg):
         else:
             LOG.debug("bf16 support not detected, disabling for this configuration.")
             cfg.bf16 = False
-            if cfg.fp16 is None:
+            if cfg.fp16 is None and not cfg.float16:
                 cfg.fp16 = True
 
     if cfg.device == "mps":

src/axolotl/utils/schemas/trl.py

@@ -67,6 +67,12 @@ class TRLConfig(BaseModel):
         default=False,
         json_schema_extra={"description": "Whether to log completions"},
     )
+    num_completions_to_print: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of completions to print. If `log_completions` is `True`, this will be the number of completions logged."
+        },
+    )
     sync_ref_model: bool | None = Field(
         default=False,
         json_schema_extra={

src/axolotl/utils/trainer.py

@@ -597,6 +597,8 @@ def prepare_optim_env(cfg):
         os.environ["ACCELERATE_MIXED_PRECISION"] = "bf16"
     elif cfg.fp16:
         os.environ["ACCELERATE_MIXED_PRECISION"] = "fp16"
+    else:
+        os.environ["ACCELERATE_MIXED_PRECISION"] = "no"
 
 
 def prepare_opinionated_env(cfg):

tests/e2e/integrations/test_llm_compressor.py

@@ -9,10 +9,14 @@ 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
+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_torch_2_4_1
+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",
@@ -31,10 +35,13 @@ 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(
             {
@@ -79,22 +86,23 @@ class TestLLMCompressorIntegration:
         )
 
         prepare_plugins(cfg)
+        cfg = validate_config(cfg)
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        try:
-        check_model_output_exists(temp_dir, cfg)
+            train(cfg=cfg, dataset_meta=dataset_meta)
-        _check_llmcompressor_model_outputs(temp_dir, save_compressed)
+            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):
-
-    # recipe.yaml should exist
-    assert (Path(temp_dir) / "recipe.yaml").exists()
-
-    # sparsity config exists if save_compressed
     if save_compressed:
+        assert (Path(temp_dir) / "recipe.yaml").exists()
+
         from compressed_tensors import ModelCompressor
         from compressed_tensors.config import Sparse24BitMaskConfig
 

tests/e2e/utils.py

@@ -105,7 +105,25 @@ def require_vllm(test_case):
             return False
 
     return unittest.skipUnless(
-        is_vllm_installed(), "test requires a vllm to be installed"
+        is_vllm_installed(), "test requires vllm to be installed"
+    )(test_case)
+
+
+def require_llmcompressor(test_case):
+    """
+    Decorator marking a test that requires a llmcompressor to be installed
+    """
+
+    def is_llmcompressor_installed():
+        try:
+            import llmcompressor  # pylint: disable=unused-import  # noqa: F401
+
+            return True
+        except ImportError:
+            return False
+
+    return unittest.skipUnless(
+        is_llmcompressor_installed(), "test requires llmcompressor to be installed"
     )(test_case)