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

.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"
+    ]
+  }
+}

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 Path(config).is_dir():
-        config = choose_config(Path(config))
+    if isinstance(config, (str, Path)):
+        config = check_remote_config(config)
+        if Path(config).is_dir():
+            config = choose_config(Path(config))
 
-    # Load the config from the yaml file
-    with open(config, encoding="utf-8") as file:
-        cfg: DictDefault = DictDefault(yaml.safe_load(file))
+        # Load the config from the yaml file
+        with open(config, encoding="utf-8") as 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/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__)
 
 

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):