fix import

optimizer_cls_and_kwargs is on trainer_kwargs
only add adamw_kwargs if they're non-null
fix mocks
better handling of override and check the optimizer
unwrap optimizer

README.md

@@ -19,9 +19,6 @@
     <br/>
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
-    <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
-    <img alt="phorm.ai" src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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">
-  </a>
 </p>
 
 Axolotl is a tool designed to streamline post-training for various AI models.

_quarto.yml

@@ -40,6 +40,7 @@ website:
 
         - section: "Deployments"
           contents:
+            - docs/docker.qmd
             - docs/multi-gpu.qmd
             - docs/multi-node.qmd
             - docs/ray-integration.qmd

docs/config.qmd

@@ -163,6 +163,12 @@ datasets:
       system: ["system"]
       tool: ["tool"]
 
+    # Optional[bool]. Whether to drop the system turn from the dataset. Only works with chat_template.
+    # This does not drop the default system message from chat_template if it exists. If you wish to,
+    # we recommend using a custom jinja template with the default system message removed or
+    # adding a system turn with empty content.
+    drop_system_message:
+
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
@@ -222,8 +228,8 @@ process_reward_model:
 chat_template: tokenizer_default
 # custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
 chat_template_jinja: null
-# Changes the default system message
+# Changes the default system message. Currently only supports chatml.
-default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
+default_system_message: You are a helpful assistant. Please give a long and detailed answer.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -445,7 +451,7 @@ gradient_checkpointing: false
 early_stopping_patience: 3
 
 # Specify a scheduler and kwargs to use with the optimizer
-lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
+lr_scheduler: # 'one_cycle' | 'rex' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:
 cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
 cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
@@ -528,6 +534,8 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 sdp_attention:
 # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
 s2_attention:
+# Optional[bool]. Whether to use low_cpu_mem_usage
+low_cpu_mem_usage:
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:
 # If resume_from_checkpoint isn't set and you simply want it to start where it left off.

docs/dataset-formats/index.qmd

@@ -129,6 +129,7 @@ You can mix and match within each approach or across approaches to train a model
 We suggest this approach when you want to bring your own tokenized dataset.
 
 Axolotl expects the dataset to have three keys:
+
 - `input_ids`: from tokenizing formatted prompt
 - `attention_mask`: for masking padding. If you don't add padding, it would be equal to `len(input_ids) * [1]`
 - `labels`: this is the same as `input_ids`, however, if you want to mask certain tokens, you would set those indices to `-100`.

docs/docker.qmd

@@ -0,0 +1,140 @@
+---
+title: "Docker"
+format:
+  html:
+    toc: true
+    toc-depth: 4
+---
+
+This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
+
+## Base
+
+The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
+
+#### Image
+
+```
+axolotlai/axolotl-base
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-base)
+
+#### Tags format
+
+```bash
+main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
+```
+
+Tags examples:
+
+- `main-base-py3.11-cu124-2.6.0`
+- `main-base-py3.11-cu124-2.5.1`
+- `main-base-py3.11-cu124-2.4.1`
+
+## Main
+
+The main image is the image that is used to run Axolotl. It is based on the `axolotlai/axolotl-base` image and includes the Axolotl codebase, dependencies, and more.
+
+#### Image
+
+```
+axolotlai/axolotl
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
+
+#### Tags format {#sec-main-tags}
+
+```bash
+# on push to main
+main-py{python_version}-cu{cuda_version}-{pytorch_version}
+
+# latest main (currently torch 2.5.1, python 3.11, cuda 12.4)
+main-latest
+
+# nightly build
+{branch}-{date_in_YYYYMMDD}-py{python_version}-cu{cuda_version}-{pytorch_version}
+
+# tagged release
+{version}
+```
+
+:::{.callout-tip}
+
+There may be some extra tags appended to the image, like `-vllm` which installs those packages.
+
+:::
+
+Tags examples:
+
+- `main-py3.11-cu124-2.6.0`
+- `main-py3.11-cu124-2.5.1`
+- `main-py3.11-cu124-2.4.1`
+- `main-latest`
+- `main-20250303-py3.11-cu124-2.6.0`
+- `main-20250303-py3.11-cu124-2.5.1`
+- `main-20250303-py3.11-cu124-2.4.1`
+- `0.7.1`
+
+## Cloud
+
+The cloud image is the image that is used to run Axolotl in the cloud. It is based on the `axolotlai/axolotl` image and sets ENV variables like HuggingFace cache directories for volume mounts, tmux, and more for different cloud providers.
+
+:::{.callout-tip}
+
+Jupyter lab is run by default. Set `JUPYTER_DISABLE=1` in the environment variables to disable it.
+
+:::
+
+#### Image
+
+```
+axolotlai/axolotl-cloud
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud)
+
+#### Tags format
+
+This uses the same tags as the [`main` image](#sec-main-tags).
+
+#### Environment variables
+
+- `JUPYTER_DISABLE`: Disable Jupyter lab.
+- `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
+- `PUBLIC_KEY`: Add a public key for the SSH service.
+- `SSH_KEY`: Add a private key for the SSH service.
+
+#### Volume mounts
+
+:::{.callout-tip}
+
+We recommend mounting volumes to `/workspace/data` for data persistence. `/workspace/axolotl` contains the source code and is ephemeral.
+
+:::
+
+- `/workspace/data/axolotl-artifacts`: Directory to store Axolotl artifacts.
+- `/workspace/data/huggingface-cache`: Directory to store HuggingFace cache.
+
+## Cloud-no-tmux
+
+This is the same as the [`cloud` image](#sec-cloud) but without tmux.
+
+#### Image
+
+```
+axolotlai/axolotl-cloud-term
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud-term)
+
+:::{.callout-note}
+
+The naming may be a bit confusing as it has `-term` appended to the end.
+
+:::
+
+#### Tags format
+
+This uses the same tags as the [`cloud` image](#sec-cloud-tags).

docs/faq.qmd

@@ -19,7 +19,9 @@ description: Frequently asked questions
 
 **Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
 
-> A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
+**Q: ModuleNotFoundError: No module named 'mpi4py' using single GPU with deepspeed**
+
+> A: You may be using deepspeed with single gpu. Please remove the `deepspeed:` section in the yaml file or `--deepspeed` CLI flag.
 
 **Q: The codes is stuck on saving preprocessed datasets.**
 

docs/installation.qmd

@@ -65,6 +65,8 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
 ```
 :::
 
+Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.
+
 ## Cloud Environments {#sec-cloud}
 
 ### Cloud GPU Providers {#sec-cloud-gpu}

docs/telemetry.qmd

@@ -1,59 +0,0 @@
----
-title: Telemetry
-description: A description of the opt-out telemetry implementation in Axolotl.
----
-
-# Telemetry in Axolotl
-
-Axolotl implements anonymous telemetry to help maintainers understand how the library
-is used and where users encounter issues. This data helps prioritize features, optimize
-performance, and fix bugs.
-
-## Data Collection
-
-We collect:
-
-- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
-version, etc.
-- Hardware info: CPU count, memory, GPU count and models
-- Runtime metrics: Training progress, memory usage, timing information
-- Usage patterns: Models (from a whitelist) and configurations used
-- Error tracking: Stack traces and error messages (sanitized to remove personal
-information)
-
-No personally identifiable information (PII) is collected.
-
-## Implementation
-
-Telemetry is implemented using PostHog and consists of:
-
-- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
-telemetry system and provides methods for tracking events.
-- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
-sends sanitized stack traces.
-- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
-runtime metrics during training.
-- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
-runtime metrics telemetry.
-
-The telemetry system will block training startup for 15 seconds to ensure users are
-aware of data collection, unless telemetry is explicitly enabled or disabled.
-
-## Opt-Out Mechanism
-
-Telemetry is **enabled by default** on an opt-out basis. To disable it, set either:
-
-- `AXOLOTL_DO_NOT_TRACK=1` (Axolotl-specific)
-- `DO_NOT_TRACK=1` (Global standard; see https://consoledonottrack.com/)
-
-To acknowledge and explicitly enable telemetry (and remove the warning message), set:
-`AXOLOTL_DO_NOT_TRACK=0`.
-
-## Privacy
-
-- All path-like config information is automatically redacted from telemetry data
-- Model information is only collected for whitelisted organizations
-    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
-- Each run generates a unique anonymous ID
-    - This allows us to link different telemetry events in a single same training run
-- Telemetry is only sent from the main process to avoid duplicate events

requirements.txt

@@ -63,6 +63,4 @@ torchao==0.7.0
 schedulefree==1.3.0
 
 axolotl-contribs-lgpl==0.0.3
-
+axolotl-contribs-mit==0.0.3
-# telemetry
-posthog>=3.15.1

src/axolotl/cli/config.py

@@ -14,8 +14,6 @@ import yaml
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.base import PluginManager
-from axolotl.telemetry.errors import send_errors
-from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
@@ -29,8 +27,6 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = logging.getLogger(__name__)
 
-TELEMETRY_MANAGER = TelemetryManager.get_instance()
-
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
     """
@@ -156,7 +152,6 @@ def prepare_plugins(cfg: DictDefault):
             plugin_manager.register(plugin_name)
 
 
-@send_errors
 def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefault:
     """
     Loads the `axolotl` configuration stored at `config`, validates it, and performs
@@ -176,7 +171,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     # Load the config from the yaml file
     with open(config, encoding="utf-8") as file:
         cfg: DictDefault = DictDefault(yaml.safe_load(file))
-    TELEMETRY_MANAGER.send_event(event_type="config-loaded", properties=cfg)
 
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
@@ -220,6 +214,4 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
 
-    TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
-
     return cfg

src/axolotl/cli/inference.py

@@ -17,7 +17,6 @@ from axolotl.cli.args import InferenceCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
-from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import (
     get_chat_template,
     get_chat_template_from_config,
@@ -43,7 +42,6 @@ def get_multi_line_input() -> str:
     return instruction
 
 
-@send_errors
 def do_inference(
     *,
     cfg: DictDefault,
@@ -137,7 +135,6 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
-@send_errors
 def do_inference_gradio(
     *,
     cfg: DictDefault,

src/axolotl/cli/merge_lora.py

@@ -12,13 +12,11 @@ from axolotl.cli.args import TrainerCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
-from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
 
 
-@send_errors
 def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     Calls `transformers`' `merge_and_unload` on the model given in the `axolotl` config

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -27,7 +27,6 @@ from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
-from axolotl.telemetry.errors import send_errors
 
 LOG = logging.getLogger(__name__)
 
@@ -121,7 +120,6 @@ def _distributed_checkpoint_to_merged_weights(
     return save_path_
 
 
-@send_errors
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,

src/axolotl/cli/preprocess.py

@@ -18,14 +18,12 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
-from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.trainer import disable_datasets_caching
 
 LOG = logging.getLogger(__name__)
 
 
-@send_errors
 def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     """
     Preprocesses dataset specified in axolotl config.

src/axolotl/cli/train.py

@@ -41,11 +41,12 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    model, tokenizer = train(cfg=cfg, dataset_meta=dataset_meta)
+    model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
     plugin_manager = PluginManager.get_instance()
 
     del model
     del tokenizer
+    del trainer
 
     plugin_manager.post_train_unload(cfg)
 

src/axolotl/common/datasets.py

@@ -10,7 +10,6 @@ from datasets import Dataset
 
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
-from axolotl.telemetry.errors import send_errors
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
@@ -25,8 +24,8 @@ class TrainDatasetMeta:
     """Dataclass with fields for training and validation datasets and metadata."""
 
     train_dataset: Dataset
-    eval_dataset: Optional[Dataset] = None
+    eval_dataset: Dataset | None = None
-    total_num_steps: Optional[int] = None
+    total_num_steps: int | None = None
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
@@ -45,7 +44,6 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
     )
 
 
-@send_errors
 def load_datasets(
     *,
     cfg: DictDefault,
@@ -105,7 +103,6 @@ def load_datasets(
     )
 
 
-@send_errors
 def load_preference_datasets(
     *,
     cfg: DictDefault,

src/axolotl/core/trainer_builder.py

@@ -35,6 +35,7 @@ from transformers import (
     EarlyStoppingCallback,
     TrainerCallback,
 )
+from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
 from axolotl.core.trainers.base import (
@@ -61,8 +62,6 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
-from axolotl.telemetry.callbacks import TelemetryCallback
-from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
@@ -86,6 +85,7 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
+from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
 
 try:
@@ -93,13 +93,11 @@ try:
 except ImportError:
     pass
 
-LOG = logging.getLogger("axolotl.core.trainer_builder")
+LOG = logging.getLogger(__name__)
 
 
 class TrainerBuilderBase(abc.ABC):
-    """
+    """Base class for trainer builder."""
-    Base class for trainer builder
-    """
 
     _train_dataset = None
     _eval_dataset = None
@@ -112,9 +110,9 @@ class TrainerBuilderBase(abc.ABC):
         self.tokenizer = tokenizer
         self.processor = processor
 
-        # in case the model supports tagging, add the axolotl tag.
+        # If the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
-        # model.push_to_hub instad of  trainer.push_to_hub.
+        # model.push_to_hub instead of trainer.push_to_hub.
         if hasattr(model, "add_model_tags"):
             model.add_model_tags(["axolotl"])
 
@@ -178,8 +176,10 @@ class TrainerBuilderBase(abc.ABC):
                 SaveAxolotlConfigtoMlflowCallback,
             )
 
-            callbacks.append(
+            callbacks.extend(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                ]
             )
         if self.cfg.use_comet and is_comet_available():
             from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
@@ -188,10 +188,6 @@ class TrainerBuilderBase(abc.ABC):
                 SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
 
-        telemetry_manager = TelemetryManager.get_instance()
-        if telemetry_manager.enabled:
-            callbacks.append(TelemetryCallback())
-
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -231,8 +227,8 @@ class TrainerBuilderBase(abc.ABC):
 
 class HFCausalTrainerBuilder(TrainerBuilderBase):
     """
-    Build the HuggingFace training args/trainer for causal models
+    Build the HuggingFace training args/trainer for causal models and reward modeling
-    and reward modelling using TRL.
+    using TRL.
     """
 
     def get_callbacks(self):
@@ -555,30 +551,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
         else:
             training_arguments_kwargs["run_name"] = None
-        training_arguments_kwargs["optim"] = (
-            self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
-        )
-        if self.cfg.optim_args:
-            if isinstance(self.cfg.optim_args, dict):
-                optim_args = ",".join(
-                    [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
-                )
-            else:
-                optim_args = self.cfg.optim_args
-            training_arguments_kwargs["optim_args"] = optim_args
-        if self.cfg.optim_target_modules:
-            training_arguments_kwargs[
-                "optim_target_modules"
-            ] = self.cfg.optim_target_modules
-        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs[
-            "loraplus_lr_embedding"
-        ] = self.cfg.loraplus_lr_embedding
-        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
-        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
-        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
-        if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
+        if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
             training_arguments_kwargs[
                 "alternate_lr_scheduler_type"
@@ -662,46 +636,114 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.reward_model:
             training_arguments_kwargs["max_length"] = self.cfg.sequence_len
 
-        # pylint: disable=duplicate-code
+        # Handle custom optimizer
-        if self.cfg.optimizer in [
+        custom_supported_optimizers = [opt.value for opt in CustomSupportedOptimizers]
-            "optimi_adamw",
+        if self.cfg.optimizer in custom_supported_optimizers:
-            "ao_adamw_4bit",
+            # Common optimizer kwargs
-            "ao_adamw_8bit",
+            optimizer_kwargs = {
-            "ao_adamw_fp8",
+                "lr": training_arguments_kwargs.get("learning_rate"),
-            "adopt_adamw",
+                "weight_decay": training_arguments_kwargs.get("weight_decay"),
-        ]:
+            }
-            # Set default so transformers doesn't throw
-            training_arguments_kwargs["optim"] = "adamw_hf"
-            training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer
 
-        if self.cfg.optimizer == "lion_pytorch":
+            # Adam-specific kwargs
-            from lion_pytorch import Lion
+            adam_kwargs = {}
+            if training_arguments_kwargs.get(
+                "adam_beta1"
+            ) and training_arguments_kwargs.get("adam_beta2"):
+                adam_kwargs["betas"] = (
+                    training_arguments_kwargs.get("adam_beta1"),
+                    training_arguments_kwargs.get("adam_beta2"),
+                )
+            if training_arguments_kwargs.get("adam_epsilon"):
+                adam_kwargs["eps"] = training_arguments_kwargs.get("adam_epsilon")
 
-            lion_kwargs = {"lr": training_arguments_kwargs["learning_rate"]}
+            if self.cfg.optimizer == "muon":
-            if "weight_decay" in training_arguments_kwargs:
+                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
-                lion_kwargs["weight_decay"] = training_arguments_kwargs["weight_decay"]
+                    MuonOptimizerFactory,
-
-            if (
-                "adam_beta1" in training_arguments_kwargs
-                and "adam_beta2" in training_arguments_kwargs
-            ):
-                lion_kwargs["betas"] = (
-                    training_arguments_kwargs["adam_beta1"],
-                    training_arguments_kwargs["adam_beta2"],
                 )
 
-            trainer_kwargs["optimizers"] = (
+                optimizer_cls = MuonOptimizerFactory
-                Lion(params=self.model.parameters(), **lion_kwargs),
+                optimizer_kwargs.update(adam_kwargs)
-                None,
+            elif self.cfg.optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                optimizer_kwargs["foreach"] = False
+                optimizer_cls = AdamW
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_4bit":
+                # TODO remove 20250401
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                optimizer_cls = AdamW4bit
+                optimizer_kwargs.update(adam_kwargs)
+
+                LOG.warning(
+                    f"`ao_adamw_4bit` will be deprecated soon. Please use `{OptimizerNames.ADAMW_TORCH_4BIT}` instead."
+                )
+            elif self.cfg.optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                optimizer_cls = AdamW8bit
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                optimizer_cls = AdamWFp8
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                optimizer_cls = ADOPT
+                adam_kwargs["decouple"] = True
+                optimizer_kwargs.update(adam_kwargs)
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optimizer_kwargs.update(self.cfg.optim_args)
+                else:
+                    # Parse string format "key1=value1,key2=value2"
+                    for mapping in self.cfg.optim_args.replace(" ", "").split(","):
+                        key, value = mapping.split("=")
+                        optimizer_kwargs[key] = value
+
+            trainer_kwargs["optimizer_cls_and_kwargs"] = (
+                optimizer_cls,
+                optimizer_kwargs,
             )
-            # Set default so transformers doesn't throw
+        else:
-            training_arguments_kwargs["optim"] = "adamw_hf"
+            # Use transformers' optimizer
+            training_arguments_kwargs["optim"] = self.cfg.optimizer
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optim_args = ",".join(
+                        [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                    )
+                else:
+                    optim_args = self.cfg.optim_args
+                training_arguments_kwargs["optim_args"] = optim_args
 
         if self.cfg.optimizer == "adamw_anyprecision":
             if Path(self.cfg.torchdistx_path).exists():
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
+        if self.cfg.optim_target_modules:
+            training_arguments_kwargs[
+                "optim_target_modules"
+            ] = self.cfg.optim_target_modules
+
+        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
+        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+
+        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
+        training_arguments_kwargs[
+            "loraplus_lr_embedding"
+        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
+
         if self.cfg.accelerator_config:
             training_arguments_kwargs[
                 "accelerator_config"
@@ -876,9 +918,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
 
 class HFRLTrainerBuilder(TrainerBuilderBase):
-    """
+    """Trainer factory class for TRL-based RLHF trainers (e.g. DPO)"""
-    Trainer factory class for TRL-based RLHF trainers (e.g. DPO)
-    """
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()

src/axolotl/core/trainers/base.py

@@ -14,6 +14,7 @@ from typing import Dict, Literal, Optional
 import torch
 from datasets import Dataset
 from peft.optimizers import create_loraplus_optimizer
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import Trainer
@@ -22,9 +23,11 @@ from transformers.utils import is_sagemaker_mp_enabled
 from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
 
+from axolotl.integrations.base import BaseOptimizerFactory
 from axolotl.monkeypatch.relora import ReLoRAScheduler
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
+    RexLR,
     get_cosine_schedule_with_min_lr,
     get_cosine_schedule_with_quadratic_warmup,
     get_cosine_schedule_with_warmup_decay_constant,
@@ -115,6 +118,17 @@ class SchedulerMixin(Trainer):
                     **extra_lr_kwargs,
                     **self.args.lr_scheduler_kwargs,
                 )
+            elif self.args.alternate_lr_scheduler_type == "rex":
+                if use_cosine_min_lr:
+                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+
+                self.lr_scheduler = RexLR(
+                    optimizer=optimizer,
+                    max_lr=self.args.learning_rate,
+                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
+                    total_steps=num_training_steps,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                )
             elif use_cosine_quadratic:
                 if use_cosine_min_lr:
                     LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
@@ -154,47 +168,18 @@ class SchedulerMixin(Trainer):
         return self.lr_scheduler
 
 
-class AxolotlTrainer(SchedulerMixin, Trainer):
+class OptimizerMixin(Trainer):
     """
-    Extend the base Trainer for axolotl helpers
+    Mixin class for shared handling of building custom optimizers
     """
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-    tag_names = ["axolotl"]
 
-    def __init__(
+    def create_optimizer_grouped_parameters(
-        self,
+        self, opt_model, optimizer_kwargs
-        *_args,
+    ) -> list[dict]:
-        bench_data_collator=None,
-        eval_data_collator=None,
-        dataset_tags=None,
-        **kwargs,
-    ):
-        self.bench_data_collator = bench_data_collator
-        self.eval_data_collator = eval_data_collator
-        self.dataset_tags = dataset_tags
-        self._signature_columns = None  # workaround for pylint
-        super().__init__(*_args, **kwargs)
-        self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
-        if self.args.orpo_alpha:
-            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-
-    def _wrap_model(self, model, training=True, dataloader=None):
-        if self.args.torch_compile:
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
-            model = torch.compile(
-                model,
-                backend=self.args.torch_compile_backend,
-                mode=self.args.torch_compile_mode,
-            )
-        return super()._wrap_model(model, training=training, dataloader=dataloader)
-
-    def create_optimizer_grouped_parameters(self, opt_model, optimizer_kwargs):
         decay_parameters = self.get_decay_parameter_names(opt_model)
-        params = {
+        params: dict = {
             "to_weight_decay": {},  # LayerNorm and bias
             "embeddings": {},  # lm_head, embed_tokens,
             "no_weight_decay": {},
@@ -281,23 +266,30 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
             and self.args.embedding_lr_scale is None
             and self.args.embedding_lr is None
             and self.args.lr_groups is None
-            and self.args.alternate_optimizer
+            and self.optimizer_cls_and_kwargs is None
-            not in [
-                "optimi_adamw",
-                "ao_adamw_8bit",
-                "ao_adamw_4bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ]
         ):
             return super().create_optimizer()
 
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+        if (
-                self.args,
+            not self.optimizer
-                opt_model,
+            and self.optimizer_cls_and_kwargs is not None
+            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
+        ):
+            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            self.optimizer = optimizer_factory_cls()(
+                opt_model, self.args, **optimizer_kwargs
             )
+
+        if not self.optimizer:
+            if self.optimizer_cls_and_kwargs is not None:
+                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            else:
+                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
+                    self.args, opt_model
+                )
+
             optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
                 opt_model, optimizer_kwargs
             )
@@ -314,50 +306,47 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
-            elif (
+            else:
-                self.args.embedding_lr_scale is not None
+                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
-                or self.args.embedding_lr is not None
+                # e.g. for GaLore optimizer.
-                or self.args.lr_groups is not None
+                if "params" in optimizer_kwargs:
-            ):
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "optimi_adamw":
-                from optimi import AdamW
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
-                    AdamW(
+                # e.g. for LOMO optimizer.
-                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
+                if "model" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
+
+                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
+                # to avoid arguments conflicts.
+                if "optimizer_dict" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop(
+                        "optimizer_dict"
                     )
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_4bit":
-                from torchao.prototype.low_bit_optim import AdamW4bit
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                self.optimizer = optimizer_cls(
-                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                    optimizer_grouped_parameters, **optimizer_kwargs
                 )
-            elif self.args.alternate_optimizer == "ao_adamw_8bit":
-                from torchao.prototype.low_bit_optim import AdamW8bit
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+            if optimizer_cls.__name__ == "Adam8bit":
-                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                import bitsandbytes
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_fp8":
-                from torchao.prototype.low_bit_optim import AdamWFp8
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                skipped = 0
-                    ADOPT(
+                for module in opt_model.modules():
-                        optimizer_grouped_parameters,
+                    if isinstance(module, nn.Embedding):
-                        decouple=True,
+                        skipped += sum(
-                        **optimizer_kwargs,
+                            {
-                    )
+                                p.data_ptr(): p.numel() for p in module.parameters()
-                )
+                            }.values()
+                        )
+                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        manager.register_module_override(
+                            module, "weight", {"optim_bits": 32}
+                        )
+                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
+                LOG.info(f"skipped: {skipped/2**20}M params")
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -366,6 +355,45 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
 
         return self.optimizer
 
+
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+    tag_names = ["axolotl"]
+
+    def __init__(
+        self,
+        *_args,
+        bench_data_collator=None,
+        eval_data_collator=None,
+        dataset_tags=None,
+        **kwargs,
+    ):
+        self.bench_data_collator = bench_data_collator
+        self.eval_data_collator = eval_data_collator
+        self.dataset_tags = dataset_tags
+        self._signature_columns = None  # workaround for pylint
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        if self.args.orpo_alpha:
+            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+
+    def _wrap_model(self, model, training=True, dataloader=None):
+        if self.args.torch_compile:
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
+            model = torch.compile(
+                model,
+                backend=self.args.torch_compile_backend,
+                mode=self.args.torch_compile_mode,
+            )
+        return super()._wrap_model(model, training=training, dataloader=dataloader)
+
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if self.args.sample_packing and not self.args.pretraining:
             if self.args.multipack_real_batches:

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

@@ -9,6 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
+from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -31,30 +32,44 @@ class GRPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         grpo_args_kwargs = {}
-        if cfg.trl and cfg.trl.use_vllm:
+
-            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
+        if not hasattr(cfg, "trl") or not cfg.trl:
-            if cfg.trl and cfg.trl.vllm_device:
+            return grpo_args_kwargs
-                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
+
-            else:
+        trl: TRLConfig = cfg.trl  # type: ignore
-                grpo_args_kwargs["vllm_device"] = "auto"
+
-            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
+        if trl.use_vllm:
+            grpo_args_kwargs["use_vllm"] = trl.use_vllm
+            grpo_args_kwargs["vllm_device"] = (
+                trl.vllm_device if trl.vllm_device else "auto"
+            )
+
+            if trl.vllm_gpu_memory_utilization:
                 grpo_args_kwargs[
                     "vllm_gpu_memory_utilization"
-                ] = cfg.trl.vllm_gpu_memory_utilization
+                ] = trl.vllm_gpu_memory_utilization
-            if cfg.trl and cfg.trl.vllm_max_model_len:
+
-                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
+            if trl.vllm_max_model_len:
-        if cfg.trl and cfg.trl.num_generations:
+                grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
-            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
+
-        if cfg.trl and cfg.trl.sync_ref_model:
+        if trl.num_generations:
-            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
+            grpo_args_kwargs["num_generations"] = trl.num_generations
-            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
+
-                grpo_args_kwargs[
+        if trl.sync_ref_model:
-                    "ref_model_mixup_alpha"
+            grpo_args_kwargs["sync_ref_model"] = trl.sync_ref_model
-                ] = cfg.trl.ref_model_mixup_alpha
+
-            if cfg.trl and cfg.trl.ref_model_sync_steps:
+            if trl.ref_model_mixup_alpha:
-                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
+                grpo_args_kwargs["ref_model_mixup_alpha"] = trl.ref_model_mixup_alpha
-        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
+
-        grpo_args_kwargs["log_completions"] = cfg.trl.log_completions
+            if trl.ref_model_sync_steps:
+                grpo_args_kwargs["ref_model_sync_steps"] = trl.ref_model_sync_steps
+
+        grpo_args_kwargs["max_completion_length"] = trl.max_completion_length
+        grpo_args_kwargs["log_completions"] = trl.log_completions
+
+        if trl.reward_weights:
+            grpo_args_kwargs["reward_weights"] = trl.reward_weights
+
         return grpo_args_kwargs
 
     @classmethod

src/axolotl/evaluate.py

@@ -10,7 +10,6 @@ import torch
 from accelerate.logging import get_logger
 
 from axolotl.logging_config import configure_logging
-from axolotl.telemetry.errors import send_errors
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.dict import DictDefault
@@ -62,7 +61,6 @@ def evaluate_dataset(
     return metrics
 
 
-@send_errors
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
     Evaluate a model on training and validation datasets

src/axolotl/integrations/base.py

@@ -23,6 +23,8 @@ import importlib
 import logging
 from typing import OrderedDict
 
+import torch
+
 
 class BasePlugin:
     """
@@ -469,3 +471,14 @@ class PluginManager:
         """
         for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)
+
+
+class BaseOptimizerFactory:
+    """
+    Base class for factories to create custom optimizers
+    """
+
+    def __call__(
+        self, opt_model, training_args, **optimizer_kwargs
+    ) -> "torch.optim.Optimizer":
+        pass

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -4,6 +4,22 @@ Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy o
 
 See https://github.com/apple/ml-cross-entropy
 
+## Requirements
+
+- PyTorch 2.4.0 or higher
+
+## Installation
+
+Run the following command to install `cut_cross_entropy[transformers]` if you don't have it already.
+
+```bash
+# if you are in dev environment
+python scripts/cutcrossentropy_install.py | sh
+
+# if you are not in dev environment
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
+```
+
 ## Usage
 
 ```yaml

src/axolotl/telemetry/callbacks.py

@@ -1,164 +0,0 @@
-"""Trainer callbacks for reporting runtime metrics at regular intervals."""
-
-import logging
-import time
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-
-from axolotl.telemetry.manager import TelemetryManager
-from axolotl.telemetry.runtime_metrics import RuntimeMetricsTracker
-
-LOG = logging.getLogger(__name__)
-
-TIME_SINCE_LAST = 30
-
-
-class TelemetryCallback(TrainerCallback):
-    """
-    Trainer callback for tracking and reporting runtime metrics.
-
-    This callback tracks training progress, runtime, and memory usage,
-    sending telemetry at configurable intervals.
-    """
-
-    report_interval_steps: int = 100
-
-    def __init__(self):
-        """Initialize the metrics callback."""
-        self.tracker = RuntimeMetricsTracker()
-        self.telemetry_manager = TelemetryManager.get_instance()
-        self.current_epoch = -1
-        self.start_time = time.time()
-        self.last_report_time = None
-        self.last_report_step = 0
-
-    def on_train_begin(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,  # pylint: disable=unused-argument
-        control: TrainerControl,  # pylint: disable=unused-argument
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        """Handle training start."""
-        self.telemetry_manager.send_event(event_type="train-started")
-
-    def on_train_end(
-        self,
-        args: TrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,
-        control: TrainerControl,  # pylint: disable=unused-argument
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        """Handle training end."""
-        # Send training completion event
-        self.telemetry_manager.send_event(
-            event_type="train-ended",
-            properties={
-                "loss": state.log_history[-1].get("loss", 0)
-                if state.log_history
-                else None,
-                "learning_rate": state.log_history[-1].get("learning_rate", 0)
-                if state.log_history
-                else None,
-            }
-            | self.tracker.metrics.to_dict(),
-        )
-
-    def on_epoch_begin(
-        self,
-        args: TrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,  # pylint: disable=unused-argument
-        control: TrainerControl,  # pylint: disable=unused-argument
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        """Handle epoch start."""
-        self.current_epoch += 1
-        self.tracker.start_epoch(self.current_epoch)
-
-    def on_epoch_end(
-        self,
-        args: TrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,  # pylint: disable=unused-argument
-        control: TrainerControl,  # pylint: disable=unused-argument
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        """Handle epoch end."""
-        self.tracker.end_epoch(self.current_epoch)
-
-    def on_step_end(
-        self,
-        args: TrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,
-        control: TrainerControl,  # pylint: disable=unused-argument
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        """Handle step end."""
-        step = state.global_step
-        self.tracker.update_step(step)
-
-        # Check if we should report metrics
-        should_report = (
-            step % self.report_interval_steps == 0
-            or step == 1  # Always report first step
-            or step - self.last_report_step >= self.report_interval_steps
-        )
-
-        if should_report:
-            current_time = time.time()
-            if self.last_report_time is not None:
-                time_since_last_report = current_time - self.last_report_time
-            else:
-                time_since_last_report = current_time - self.start_time
-            steps_since_last_report = step - self.last_report_step
-
-            # Only report if enough time has passed to avoid flooding
-            if (
-                step == 1
-                or time_since_last_report >= TIME_SINCE_LAST
-                or steps_since_last_report >= self.report_interval_steps
-            ):
-                # Calculate steps per second for this interval
-                if time_since_last_report > 0 and steps_since_last_report > 0:
-                    steps_per_second = steps_since_last_report / time_since_last_report
-                else:
-                    steps_per_second = 0
-
-                # Update memory metrics
-                self.tracker.update_memory_metrics()
-
-                loss = state.log_history[-1].get("loss", 0) if state.log_history else 0
-                learning_rate = (
-                    state.log_history[-1].get("learning_rate", 0)
-                    if state.log_history
-                    else 0
-                )
-
-                # Prepare metrics to report
-                metrics = {
-                    "step": step,
-                    "epoch": self.current_epoch,
-                    "progress": state.epoch,  # Fractional epoch progress
-                    "loss": loss,
-                    "learning_rate": learning_rate,
-                    "steps_per_second": steps_per_second,
-                    "elapsed_time": current_time - self.start_time,
-                    "time_since_last_report": time_since_last_report,
-                }
-
-                # Add memory metrics
-                memory_metrics = self.tracker.get_memory_metrics()
-                metrics.update({"memory": memory_metrics})
-
-                # Send telemetry
-                self.telemetry_manager.send_event(
-                    event_type="train-progress", properties=metrics
-                )
-
-                # Update last report time and step
-                self.last_report_time = current_time
-                self.last_report_step = step

src/axolotl/telemetry/errors.py

@@ -1,160 +0,0 @@
-"""Telemetry utilities for exception and traceback information."""
-
-import logging
-import os
-import re
-import traceback
-from functools import wraps
-from inspect import getmodule
-from typing import Any, Callable
-
-from axolotl.telemetry.manager import TelemetryManager
-
-LOG = logging.getLogger(__name__)
-
-ERROR_HANDLED = False
-
-
-def sanitize_stack_trace(stack_trace: str) -> str:
-    """
-    Remove personal information from stack trace messages while keeping Python package codepaths.
-
-    This function identifies Python packages by looking for common patterns in virtual environment
-    and site-packages directories, preserving the package path while removing user-specific paths.
-
-    Args:
-        stack_trace: The original stack trace string.
-
-    Returns:
-        A sanitized version of the stack trace with Python package paths preserved.
-    """
-    # Split the stack trace into lines to process each file path separately
-    lines = stack_trace.split("\n")
-    sanitized_lines = []
-
-    # Regular expression to find file paths in the stack trace
-    path_pattern = re.compile(r'(?:File ")(.*?)(?:")')
-
-    # Regular expression to identify paths in site-packages or dist-packages
-    # This matches path segments like "site-packages/package_name" or "dist-packages/package_name"
-    site_packages_pattern = re.compile(
-        r"(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
-    )
-
-    # Additional common virtual environment patterns
-    venv_lib_pattern = re.compile(
-        r"(?:lib|Lib)[/\\](?:python\d+(?:\.\d+)?[/\\])?(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
-    )
-
-    for line in lines:
-        # Check if this line contains a file path
-        path_match = path_pattern.search(line)
-
-        if path_match:
-            full_path = path_match.group(1)
-            sanitized_path = ""
-
-            # Try to match site-packages pattern
-            site_packages_match = site_packages_pattern.search(full_path)
-            venv_lib_match = venv_lib_pattern.search(full_path)
-
-            if site_packages_match:
-                # Find the index where the matched pattern starts
-                idx = full_path.find("site-packages")
-                if idx == -1:
-                    idx = full_path.find("dist-packages")
-
-                # Keep from 'site-packages' onward
-                if idx >= 0:
-                    sanitized_path = full_path[idx:]
-            elif venv_lib_match:
-                # For other virtual environment patterns, find the package directory
-                match_idx = venv_lib_match.start(1)
-                if match_idx > 0:
-                    # Keep from the package name onward
-                    package_name = venv_lib_match.group(1)
-                    idx = full_path.rfind(
-                        package_name, 0, match_idx + len(package_name)
-                    )
-                    if idx >= 0:
-                        sanitized_path = full_path[idx:]
-
-            # If we couldn't identify a package pattern but path contains 'axolotl'
-            elif "axolotl" in full_path:
-                idx = full_path.rfind("axolotl")
-                if idx >= 0:
-                    sanitized_path = full_path[idx:]
-
-            # Apply the sanitization to the line
-            if sanitized_path:
-                line = line.replace(full_path, sanitized_path)
-            else:
-                # If we couldn't identify a package pattern, just keep the filename
-                filename = os.path.basename(full_path)
-                if filename:
-                    line = line.replace(full_path, filename)
-                else:
-                    line = line.replace(full_path, "")
-
-        sanitized_lines.append(line)
-
-    return "\n".join(sanitized_lines)
-
-
-def send_errors(func: Callable) -> Callable:
-    """
-    Decorator to send exception info in a function. If an exception is raised, we send
-    telemetry containing the stack trace and error message.
-
-    If an error occurs in a decorated function that is called by another decorated
-    function, we'll only send telemetry corresponding to the lower-level function.
-
-    Args:
-        func: Function to decorate.
-
-    Returns:
-        Decorated function.
-    """
-
-    @wraps(func)
-    def wrapper(*args, **kwargs) -> Any:
-        telemetry_manager = TelemetryManager.get_instance()
-
-        if not telemetry_manager.enabled:
-            return func(*args, **kwargs)
-
-        try:
-            return func(*args, **kwargs)
-        except Exception as exception:
-            # Only track if we're not already handling an error. This prevents us from
-            # capturing an error more than once in nested decorated function calls.=
-            global ERROR_HANDLED  # pylint: disable=global-statement
-            if not ERROR_HANDLED:
-                ERROR_HANDLED = True
-
-                # Get function module path
-                module = getmodule(func)
-                module_path = (
-                    f"{module.__name__}.{func.__name__}" if module else func.__name__
-                )
-
-                # Get stack trace
-                stack_trace = "".join(
-                    traceback.format_exception(
-                        type(exception), exception, exception.__traceback__
-                    )
-                )
-                stack_trace = sanitize_stack_trace(stack_trace)
-
-                # Send error telemetry
-                telemetry_manager.send_event(
-                    event_type=f"{module_path}-errored",
-                    properties={
-                        "exception": str(exception),
-                        "stack_trace": stack_trace,
-                    },
-                )
-
-            raise
-
-    return wrapper

src/axolotl/telemetry/manager.py

@@ -1,399 +0,0 @@
-"""Telemetry manager and associated utilities."""
-
-import atexit
-import importlib
-import logging
-import os
-import platform
-import time
-import uuid
-from pathlib import Path
-from typing import Any
-
-import posthog
-import psutil
-import torch
-import yaml
-
-LOG = logging.getLogger(__name__)
-
-POSTHOG_HOST = "https://app.posthog.com"
-POSTHOG_WRITE_KEY = "phc_1kUR0o04oJKKTTeSsIz2Mfm5mpiVsQEf2WOlzljMD7y"
-
-ENABLED_WARNING_SLEEP_SECONDS = 15
-ENABLED_WARNING = (
-    "\nTelemetry is enabled. This helps Axolotl's maintainers by providing insights into:\n"
-    "- Which models and configurations are most commonly used\n"
-    "- What hardware setups need to be supported\n"
-    "- Where users encounter errors\n\n"
-    "This data helps us prioritize features, optimize performance, and fix bugs.\n\n"
-    "To disable telemetry, set either:\n"
-    "- AXOLOTL_DO_NOT_TRACK=1 (Axolotl-specific)\n"
-    "- DO_NOT_TRACK=1 (Global standard; see https://consoledonottrack.com/)\n\n"
-    "To remove this warning and continue with telemetry enabled,"
-    "explicitly set AXOLOTL_DO_NOT_TRACK=0 (and leave DO_NOT_TRACK unset / set to 0)\n\n"
-    "No personally identifiable information is collected."
-    "For details, see: https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html\n\n"
-    f"Sleeping for {ENABLED_WARNING_SLEEP_SECONDS}s..."
-)
-
-WHITELIST_PATH = str(Path(__file__).parent / "whitelist.yaml")
-
-# NOTE: Keep these up to date with any config schema changes
-FIELDS_WITH_ORGS = {
-    "base_model",
-    "tokenizer_config",
-    "base_model_config",
-    "pretraining_dataset",  # NOTE: this field may be a string or a dictionary
-}
-FIELDS_TO_REDACT = {"resume_from_checkpoint", "hub_model_id"}
-PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_"}
-PATH_INDICATORS = {"path", "dir"}
-
-# pylint: disable=duplicate-code
-RELEVANT_PACKAGES = {
-    "torch",
-    "transformers",
-    "trl",
-    "datasets",
-    "peft",
-    "bitsandbytes",
-    "accelerate",
-    "optimum",
-    "deepspeed",
-    "ray",
-    "axolotl",
-    "triton",
-    "mamba-ssm",
-    "flash-attn",
-    "xformers",
-    "autoawq",
-    "tokenizers",
-    "sentencepiece",
-    "torchao",
-    "lm_eval",
-}
-
-
-def is_main_process() -> bool:
-    """
-    Check whether we're running in the main process.
-
-    Note:
-        We're using this function instead of `torch.utils.distributed.is_main_process`
-        causes issues with DeepSpeed world_size since. This function avoids that issue
-        by checking env vars that are set by various launchers.
-
-    Returns:
-        Whether we're running in the main process.
-    """
-    # If PyTorch distributed is already initialized, use it
-    if torch.distributed.is_initialized():
-        return torch.distributed.get_rank() == 0
-
-    # Otherwise check environment variables for global rank
-    # NOTE: need to verify this in SLURM / OpenMPI environments
-    global_rank = int(
-        os.environ.get(
-            "RANK",
-            os.environ.get(
-                "GLOBAL_RANK",
-                os.environ.get(
-                    "SLURM_PROCID",
-                    os.environ.get(
-                        "OMPI_COMM_WORLD_RANK",
-                        "0",
-                    ),
-                ),
-            ),
-        )
-    )
-
-    return global_rank == 0
-
-
-class TelemetryManager:
-    """Manages telemetry collection and transmission"""
-
-    _instance = None
-    _initialized = False
-
-    def __new__(cls):
-        """
-        Telemetry manager constructor. Creates the singleton instance of this class if
-        it doesn't already exist.
-        """
-        if cls._instance is None:
-            cls._instance = super(TelemetryManager, cls).__new__(cls)
-            cls._instance._initialized = False
-
-        return cls._instance
-
-    def __init__(self):
-        """Telemetry manager initializer"""
-        if self._initialized:
-            return
-
-        self.enabled, self.explicit_enable = self._check_telemetry_enabled()
-
-        if self.enabled:
-            self.run_id = str(uuid.uuid4())
-            self.whitelist = self._load_whitelist()
-
-            try:
-                self.system_info = self._get_system_info()
-            except Exception as e:  # pylint: disable=broad-exception-caught
-                LOG.warning(f"Error during system info collection: {e}")
-                self.system_info = None
-
-            self._init_posthog()
-
-            # Register shutdown method to flush posthog telemetry
-            atexit.register(self.shutdown)
-
-        self._initialized = True
-
-    @classmethod
-    def get_instance(cls) -> "TelemetryManager":
-        if cls._instance is None:
-            cls._instance = TelemetryManager()
-
-        return cls._instance
-
-    def _check_telemetry_enabled(self) -> tuple[bool, bool]:
-        """
-        Check if telemetry is enabled based on environment variables. We also check
-        whether this is the main process (for the distributed setting and to avoid
-        sending duplicate PostHog events per GPU).
-
-        Note: This is enabled by default on an opt-out basis. Set either
-        `AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1` to disable telemetry. For more
-        details, see https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html.
-
-        Returns:
-            Tuple containing:
-                - Boolean denoting whether telemetry is enabled or disabled.
-                - Boolean denoting whether telemetry is explicitly enabled or not.
-        """
-        # Parse relevant env vars and fill opt-out default values
-        axolotl_do_not_track = os.getenv("AXOLOTL_DO_NOT_TRACK")
-        do_not_track = os.getenv("DO_NOT_TRACK")
-
-        # If explicitly enabled, we'll disable the telemetry warning message
-        explicit_enabled = axolotl_do_not_track in ["0", "false"]
-
-        if axolotl_do_not_track is None:
-            axolotl_do_not_track = "0"
-
-        if do_not_track is None:
-            do_not_track = "0"
-
-        # Respect AXOLOTL_DO_NOT_TRACK, DO_NOT_TRACK if enabled
-        enabled = axolotl_do_not_track.lower() not in (
-            "1",
-            "true",
-        ) and do_not_track.lower() not in ("1", "true")
-
-        # Show warning (and sleep on all ranks) unless explicitly enabled
-        if enabled and not explicit_enabled:
-            if is_main_process():
-                LOG.warning(ENABLED_WARNING)
-            time.sleep(ENABLED_WARNING_SLEEP_SECONDS)
-
-        # Only rank 0 will send telemetry
-        if not is_main_process():
-            return False, False
-
-        return enabled, explicit_enabled
-
-    def _load_whitelist(self) -> dict:
-        """Load HuggingFace Hub organization whitelist"""
-        with open(WHITELIST_PATH, encoding="utf-8") as f:
-            return yaml.safe_load(f)
-
-    def _is_whitelisted(self, base_model: str) -> bool:
-        """Check if model org is in whitelist"""
-        if not base_model:
-            return False
-
-        base_model = base_model.lower()
-        return any(
-            org.lower() in base_model for org in self.whitelist.get("organizations", [])
-        )
-
-    def _init_posthog(self):
-        """Initialize PostHog client"""
-        posthog.host = POSTHOG_HOST
-        posthog.project_api_key = POSTHOG_WRITE_KEY
-
-    def _redact_paths(self, properties: dict[str, Any]) -> dict[str, Any]:
-        """
-        Redact properties to remove any paths, so as to avoid inadvertently collecting
-        private or personally identifiable information (PII). We also remove
-        information related to Wandb, MLflow, etc. configuration.
-
-        Args:
-            properties: Dictionary of properties to redact.
-
-        Returns:
-            Properties dictionary with redaction applied.
-        """
-        if not properties:
-            return {}
-
-        def redact_value(value: Any, key: str = "") -> Any:
-            """Recursively sanitize values, redacting those with path-like keys"""
-            if isinstance(key, str) and isinstance(value, str):
-                # Fields that should be redacted if org is not whitelisted
-                if key in FIELDS_WITH_ORGS:
-                    org = value.split("/")[0]
-                    if org not in self.whitelist["organizations"]:
-                        return "[REDACTED]"
-
-                # Other redaction special cases
-                if (
-                    key in FIELDS_TO_REDACT
-                    or any(prefix in key for prefix in PREFIXES_TO_REDACT)
-                    or any(indicator in key.lower() for indicator in PATH_INDICATORS)
-                ):
-                    return "[REDACTED]"
-
-            # Handle nested structures
-            if isinstance(value, dict):
-                return {k: redact_value(v, k) for k, v in value.items()}
-            if isinstance(value, list):
-                return [redact_value(item) for item in value]
-
-            return value
-
-        # Create new dict with redacted values
-        redacted = {k: redact_value(v, k) for k, v in properties.items()}
-
-        return redacted
-
-    def _get_system_info(self) -> dict[str, Any]:
-        """Collect system information for various hardware accelerators"""
-        gpu_info = []
-        accelerator_type = "none"
-
-        # NVIDIA GPUs
-        if torch.cuda.is_available():
-            accelerator_type = "cuda"
-            for i in range(torch.cuda.device_count()):
-                gpu_info.append(
-                    {
-                        "name": torch.cuda.get_device_name(i),
-                        "memory": torch.cuda.get_device_properties(i).total_memory,
-                    }
-                )
-
-        # AMD GPUs
-        elif hasattr(torch, "hip") and torch.hip.is_available():
-            accelerator_type = "hip"
-            for i in range(torch.hip.device_count()):
-                gpu_info.append(
-                    {
-                        "name": torch.hip.get_device_name(i),
-                        "memory": torch.hip.get_device_properties(i).total_memory
-                        if hasattr(torch.hip, "get_device_properties")
-                        else None,
-                    }
-                )
-
-        # Apple Silicon
-        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            accelerator_type = "mps"
-            gpu_info.append(
-                {
-                    "name": "Apple Silicon",
-                    # NOTE: this is memory allocated to this process, not total memory
-                    "memory": torch.mps.driver_allocated_memory(),
-                }
-            )
-
-        # Intel GPUs
-        elif hasattr(torch, "xpu") and torch.xpu.is_available():
-            accelerator_type = "xpu"
-            for i in range(torch.xpu.device_count()):
-                memory = None
-                if hasattr(torch.xpu, "get_device_properties"):
-                    memory = torch.xpu.get_device_properties(i).total_memory
-
-                gpu_info.append(
-                    {
-                        "name": torch.xpu.get_device_name(i),
-                        "memory": memory,
-                    }
-                )
-
-        # NPUs
-        elif hasattr(torch, "npu") and torch.npu.is_available():
-            accelerator_type = "npu"
-            for i in range(torch.npu.device_count()):
-                memory = None
-                if hasattr(torch.npu, "get_device_properties"):
-                    memory = torch.npu.get_device_properties(i).total_memory
-
-                gpu_info.append(
-                    {
-                        "name": torch.npu.get_device_name(i),
-                        "memory": memory,
-                    }
-                )
-
-        # Get relevant package versions
-        installed_packages = {}
-        for package in RELEVANT_PACKAGES:
-            try:
-                version = importlib.metadata.version(package)
-                installed_packages[f"{package}_version"] = version
-            except importlib.metadata.PackageNotFoundError:
-                pass
-
-        return {
-            "os": platform.system(),
-            "python_version": platform.python_version(),
-            "cpu_count": psutil.cpu_count(),
-            "memory_total": psutil.virtual_memory().total,
-            "accelerator_type": accelerator_type,
-            "accelerator_count": len(gpu_info),
-            "accelerator_info": gpu_info,
-            **installed_packages,
-        }
-
-    def send_event(self, event_type: str, properties: dict[str, Any] | None = None):
-        """Send a telemetry event"""
-        if not self.enabled:
-            return
-
-        if properties is None:
-            properties = {}
-
-        # Sanitize properties to remove PII
-        properties = self._redact_paths(properties)
-
-        # Wrap PostHog errors in try / except to not raise errors during Axolotl usage
-        try:
-            # Send event via PostHog
-            posthog.capture(
-                distinct_id=self.run_id,
-                event=event_type,
-                properties=properties,
-                disable_geoip=True,
-            )
-        except Exception as e:  # pylint: disable=broad-exception-caught
-            LOG.warning(f"Failed to send telemetry event: {e}")
-
-        # Additionally, send system info telemetry when loading config.
-        # NOTE: Is this the best place for this?
-        if event_type == "config-loaded":
-            self.send_system_info()
-
-    def send_system_info(self):
-        """Helper method for sending system info"""
-        self.send_event(event_type="system-info", properties=self.system_info)
-
-    def shutdown(self):
-        """Ensure all queued events are processed before shutdown"""
-        if self.enabled:
-            posthog.flush()

src/axolotl/telemetry/runtime_metrics.py

@@ -1,209 +0,0 @@
-"""Telemetry utilities for runtime and memory metrics."""
-
-import logging
-import time
-from dataclasses import dataclass, field
-from typing import Any
-
-import psutil
-import torch
-
-from axolotl.telemetry.manager import TelemetryManager
-
-LOG = logging.getLogger(__name__)
-
-
-@dataclass
-class RuntimeMetrics:
-    """Container for runtime metrics to be tracked throughout training."""
-
-    # Timing metrics
-    start_time: float
-    epoch_start_times: dict[int, float] = field(init=False)
-    epoch_end_times: dict[int, float] = field(init=False)
-
-    # Memory metrics
-    peak_cpu_memory: int = 0
-    peak_gpu_memory: dict[int, int] = field(init=False)
-
-    # Progress metrics
-    total_steps: int = 0
-    current_epoch: int = 0
-    current_step: int = 0
-
-    def __post_init__(self):
-        """Initialize empty metric mappings."""
-        self.epoch_start_times = {}
-        self.epoch_end_times = {}
-        self.peak_gpu_memory = {}
-
-    @property
-    def elapsed_time(self) -> float:
-        """Calculate total elapsed time in seconds."""
-        return time.time() - self.start_time
-
-    def epoch_time(self, epoch: int) -> float | None:
-        """Calculate time taken for a specific epoch in seconds."""
-        if epoch in self.epoch_start_times and epoch in self.epoch_end_times:
-            return self.epoch_end_times[epoch] - self.epoch_start_times[epoch]
-
-        return None
-
-    def average_epoch_time(self) -> float | None:
-        """Calculate average time per epoch in seconds."""
-        completed_epochs = [
-            epoch for epoch in self.epoch_start_times if epoch in self.epoch_end_times
-        ]
-        if not completed_epochs:
-            return None
-
-        total_time = 0.0
-        for epoch in completed_epochs:
-            epoch_time = self.epoch_time(epoch)
-            if epoch_time is not None:  # Check to avoid mypy warning
-                total_time += epoch_time
-
-        return total_time / len(completed_epochs)
-
-    def steps_per_second(self) -> float | None:
-        """Calculate average steps per second across all training."""
-        if self.total_steps == 0 or self.elapsed_time == 0:
-            return None
-
-        return self.total_steps / self.elapsed_time
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert metrics to a dictionary for telemetry reporting."""
-        metrics = {
-            "total_time_seconds": self.elapsed_time,
-            "total_steps": self.total_steps,
-            "steps_per_second": self.steps_per_second(),
-            "epochs_completed": len(
-                [
-                    epoch
-                    for epoch in self.epoch_start_times
-                    if epoch in self.epoch_end_times
-                ]
-            ),
-            "peak_cpu_memory_bytes": self.peak_cpu_memory,
-        }
-
-        # Add per-epoch timing if available
-        epoch_times: dict[str, float] = {}
-        for epoch in sorted(self.epoch_end_times.keys()):
-            time_taken = self.epoch_time(epoch)
-            if time_taken is not None:
-                epoch_times[f"epoch_{epoch}_seconds"] = time_taken
-
-        if epoch_times:
-            metrics["epoch_times"] = epoch_times  # type: ignore
-            metrics["average_epoch_time_seconds"] = self.average_epoch_time()
-
-        # Add GPU memory metrics if available
-        if self.peak_gpu_memory:
-            gpu_metrics: dict[str, int] = {}
-            for gpu_id, memory in self.peak_gpu_memory.items():
-                gpu_metrics[f"gpu_{gpu_id}_peak_memory_bytes"] = memory
-            metrics["gpu_memory"] = gpu_metrics  # type: ignore
-
-        return metrics
-
-
-class RuntimeMetricsTracker:
-    """Tracker for runtime metrics during training."""
-
-    update_interval = 100
-
-    def __init__(self):
-        """Initialize the runtime metrics tracker."""
-        self.metrics = RuntimeMetrics(start_time=time.time())
-        self.telemetry_manager = TelemetryManager.get_instance()
-
-    def start_epoch(self, epoch: int):
-        """Record the start of a new epoch."""
-        self.metrics.current_epoch = epoch
-        self.metrics.epoch_start_times[epoch] = time.time()
-        self.update_memory_metrics()
-
-    def end_epoch(self, epoch: int):
-        """Record the end of an epoch."""
-        self.metrics.epoch_end_times[epoch] = time.time()
-
-    def update_step(self, step: int):
-        """Update the current step count."""
-        self.metrics.current_step = step
-        self.metrics.total_steps += 1
-
-        # Periodically update memory metrics
-        if step % self.update_interval == 0:
-            self.update_memory_metrics()
-
-    def _get_allocated_memory(self) -> dict[int, int]:
-        """
-        Helper function for getting accelerator-agnostic allocated memory.
-
-        Returns:
-            A dictionary mapping device IDs to allocated memory in bytes
-        """
-        memory_used: dict[int, int] = {}
-
-        # NVIDIA GPUs
-        if torch.cuda.is_available():
-            for i in range(torch.cuda.device_count()):
-                memory_used[i] = torch.cuda.memory_allocated(i)
-
-        # AMD GPUs
-        elif hasattr(torch, "hip") and torch.hip.is_available():
-            for i in range(torch.hip.device_count()):
-                if hasattr(torch.hip, "memory_allocated"):
-                    memory_used[i] = torch.hip.memory_allocated(i)
-
-        # Apple Silicon
-        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            # MPS doesn't have per-device memory stats since there's only one device
-            if hasattr(torch.mps, "current_allocated_memory"):
-                memory_used[0] = torch.mps.current_allocated_memory()
-
-        # Intel GPUs
-        elif hasattr(torch, "xpu") and torch.xpu.is_available():
-            for i in range(torch.xpu.device_count()):
-                if hasattr(torch.xpu, "memory_allocated"):
-                    memory_used[i] = torch.xpu.memory_allocated(i)
-
-        # NPUs
-        elif hasattr(torch, "npu") and torch.npu.is_available():
-            for i in range(torch.npu.device_count()):
-                if hasattr(torch.npu, "memory_allocated"):
-                    memory_used[i] = torch.npu.memory_allocated(i)
-
-        return memory_used
-
-    def update_memory_metrics(self):
-        """Update peak memory usage metrics."""
-        # CPU memory
-        cpu_memory = psutil.Process().memory_info().rss
-        self.metrics.peak_cpu_memory = max(self.metrics.peak_cpu_memory, cpu_memory)
-
-        # GPU memory (if available)
-        memory_used = self._get_allocated_memory()
-        for i, memory in memory_used.items():
-            self.metrics.peak_gpu_memory[i] = max(
-                self.metrics.peak_gpu_memory.get(i, 0), memory
-            )
-
-    def get_memory_metrics(self) -> dict[str, Any]:
-        """Get the current memory metrics as a dictionary."""
-        memory_metrics = {
-            "cpu_memory_bytes": psutil.Process().memory_info().rss,
-            "peak_cpu_memory_bytes": self.metrics.peak_cpu_memory,
-        }
-
-        # GPU memory (if available)
-        memory_used = self._get_allocated_memory()
-        for i, memory in memory_used.items():
-            memory_metrics[f"gpu_{i}_memory_bytes"] = memory
-            memory_metrics[
-                f"gpu_{i}_peak_memory_bytes"
-            ] = self.metrics.peak_gpu_memory.get(i, 0)
-
-        return memory_metrics

src/axolotl/telemetry/whitelist.yaml

@@ -1,18 +0,0 @@
-organizations:
-  - "axolotl-ai-co"
-  - "meta-llama"
-  - "huggingface"
-  - "nvidia"
-  - "facebook"
-  - "google"
-  - "microsoft"
-  - "deepseek-ai"
-  - "HuggingFaceTB"
-  - "mistralai"
-  - "Qwen"
-  - "briaai"
-  - "unsloth"
-  - "NousResearch"
-  - "allenai"
-  - "amd"
-  - "tiiuae"

src/axolotl/train.py

@@ -7,23 +7,24 @@ import signal
 import sys
 import weakref
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Any
 
 import torch
 import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
-from peft import PeftModel
+from datasets import Dataset
-from transformers import PreTrainedModel, PreTrainedTokenizer
+from peft import PeftConfig, PeftModel
+from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from transformers.trainer import Trainer
 
 from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl.unsloth import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
+from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.logging_config import configure_logging
-from axolotl.telemetry.errors import send_errors
-from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
@@ -34,20 +35,25 @@ try:
 except ImportError:
     BetterTransformer = None
 
-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__)
 
-TELEMETRY_MANAGER = TelemetryManager.get_instance()
 
+def setup_model_and_tokenizer(
+    cfg: DictDefault,
+) -> tuple[
+    PreTrainedModel, PreTrainedTokenizer, PeftConfig | None, ProcessorMixin | None
+]:
+    """
+    Load the tokenizer, processor (for multimodal models), and model based on configuration.
 
-@send_errors
+    Args:
-def train(
+        cfg: Dictionary mapping `axolotl` config keys to values.
-    *, cfg: DictDefault, dataset_meta: TrainDatasetMeta
+
-) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
+    Returns:
+        Tuple containing model, tokenizer, `peft_config` (if LoRA / QLoRA, else
+            `None`), and processor (if multimodal, else `None`).
+    """
     # Load tokenizer
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
@@ -60,11 +66,58 @@ def train(
     if cfg.is_multimodal:
         processor = load_processor(cfg, tokenizer)
 
-    # Get datasets
+    # Load the model and peft_config
-    train_dataset = dataset_meta.train_dataset
+    msg = "loading model"
-    eval_dataset = dataset_meta.eval_dataset
+    if cfg.adapter:
-    total_num_steps = dataset_meta.total_num_steps
+        msg += " and peft_config..."
+    LOG.debug(msg)
 
+    model, peft_config = load_model(cfg, tokenizer, processor=processor)
+    if model.generation_config is not None:
+        model.generation_config.do_sample = True
+
+    # Apply freezing if specified
+    if cfg.unfrozen_parameters:
+        freeze_layers_except(model, cfg.unfrozen_parameters)
+
+    return model, tokenizer, peft_config, processor
+
+
+def setup_reference_model(
+    cfg: DictDefault, tokenizer: PreTrainedTokenizer
+) -> PreTrainedModel | None:
+    """
+    Set up the reference model for RL training if needed.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        tokenizer: The tokenizer to use for the reference model.
+
+    Returns:
+        Reference model if needed for RL training, `None` otherwise.
+    """
+    model_ref = None
+    if cfg.rl and cfg.rl != "orpo":
+        if cfg.adapter and not cfg.rl_adapter_ref_model:
+            # use built-in trl autounwrap
+            LOG.debug("Passing model_ref: None to RL trainer")
+            model_ref = None  # explicit setting to None
+        else:
+            # load the model again for model_ref/baseline
+            model_ref, _ = load_model(cfg, tokenizer, reference_model=True)
+    return model_ref
+
+
+def determine_resume_checkpoint(cfg: DictDefault) -> str | None:
+    """
+    Determine the checkpoint to resume from based on configuration.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        Path to the checkpoint to resume from, or `None` if not resuming.
+    """
     if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
         possible_checkpoints = [
             str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
@@ -78,85 +131,22 @@ def train(
             LOG.info(
                 f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
             )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
+    return cfg.resume_from_checkpoint
 
-    # Load model
-    msg = "loading model"
-    if cfg.adapter:
-        msg += " and peft_config..."
-    LOG.debug(msg)
-    model, peft_config = load_model(cfg, tokenizer, processor=processor)
-    if model.generation_config is not None:
-        model.generation_config.do_sample = True
 
-    TELEMETRY_MANAGER.send_event(
+def setup_signal_handler(
-        event_type="model-loaded", properties=model.config.to_dict()
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
-    )
+):
-    if peft_config:
+    """
-        TELEMETRY_MANAGER.send_event(
+    Set up signal handler for graceful termination.
-            event_type="peft-config-loaded", properties=peft_config.to_dict()
-        )
 
-    model_ref = None
+    Args:
-    if cfg.rl and cfg.rl != "orpo":
+        cfg: Dictionary mapping `axolotl` config keys to values.
-        if cfg.adapter and not cfg.rl_adapter_ref_model:
+        model: The model to save on termination
-            # use built-in trl autounwrap
+        safe_serialization: Whether to use safe serialization when saving
-            LOG.debug("Passing model_ref: None to RL trainer")
+    """
-            model_ref = None  # explicit setting to None
+    # ray workers don't have access to this signal
-        else:
+    if cfg.local_rank == 0 and not cfg.use_ray:
-            # load the model again for model_ref / baseline
-            model_ref, _ = load_model(cfg, tokenizer, reference_model=True)
-
-    safe_serialization = cfg.save_safetensors is True
-
-    if cfg.unfrozen_parameters:
-        freeze_layers_except(model, cfg.unfrozen_parameters)
-
-    trainer = setup_trainer(
-        cfg,
-        train_dataset,
-        eval_dataset,
-        (model, model_ref, peft_config),
-        tokenizer,
-        processor,
-        total_num_steps,
-    )
-
-    if cfg.fix_untrained_tokens:
-        # check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
-        sig = inspect.signature(fix_untrained_tokens)
-        # if the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
-        if "token_ids_to_fix" in sig.parameters and isinstance(
-            cfg.fix_untrained_tokens, list
-        ):
-            fix_untrained_tokens(
-                model,
-                tokenizer,
-                train_dataset,
-                token_ids_to_fix=cfg.fix_untrained_tokens,
-            )
-        else:
-            fix_untrained_tokens(model, tokenizer, train_dataset)
-        if cfg.local_rank == 0:
-            model.save_pretrained(
-                str(Path(cfg.output_dir)), safe_serialization=safe_serialization
-            )
-
-    # go ahead and presave, so we have the adapter config available to inspect
-    if peft_config:
-        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
-        peft_config.save_pretrained(cfg.output_dir)
-    # additionally presave the tokenizer and model configs
-    if not Path(cfg.output_dir).is_dir():
-        os.makedirs(cfg.output_dir, exist_ok=True)
-    tokenizer.save_pretrained(str(Path(cfg.output_dir)))
-    if hasattr(model, "config"):
-        model.config.save_pretrained(str(Path(cfg.output_dir)))
-
-    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
-    if (
-        cfg.local_rank == 0 and not cfg.use_ray
-    ):  # ray workers don't have access to this signal
 
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
@@ -174,15 +164,18 @@ def train(
             lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
         )
 
-    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)"""
-    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
 
-    if getattr(cfg, "axolotl_config_path"):
+def execute_training(
-        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
+    cfg: DictDefault, trainer: Any, resume_from_checkpoint: str | None
-        version = importlib.metadata.version("axolotl")
+):
-        if raw_axolotl_cfg.is_file():
+    """
-            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
+    Execute the training process with appropriate backend configurations.
 
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The configured trainer object.
+        resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
+    """
     LOG.info("Starting trainer...")
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
@@ -197,13 +190,31 @@ def train(
             trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
 
-    # post training
+
+def save_trained_model(
+    cfg: DictDefault,
+    trainer: Any,
+    model: PreTrainedModel,
+    safe_serialization: bool,
+):
+    """
+    Save the trained model according to configuration and training setup.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The trainer object.
+        model: The trained model to save.
+        safe_serialization: Whether to use safe serialization.
+    """
+    LOG.info(f"Training completed! Saving pre-trained model to {cfg.output_dir}.")
+
+    # Post training module hooks
     for name, module in model.named_modules():
         if hasattr(module, "_post_training"):
             module._post_training(model, name)  # pylint: disable=protected-access
 
+    # Handle FSDP state dict type
     state_dict_type = "FULL_STATE_DICT"
     if trainer.is_fsdp_enabled:
         if cfg.fsdp_final_state_dict_type:
@@ -211,16 +222,18 @@ def train(
         trainer.accelerator.state.fsdp_plugin.set_state_dict_type(state_dict_type)
         LOG.info(f"Set FSDP state dict type to {state_dict_type} for saving.")
 
+    # Handle ReLoRA early return case
     if cfg.relora_steps:
         if cfg.adapter == "lora" and not (cfg.load_in_4bit or cfg.load_in_8bit):
             model = model.merge_and_unload()
         else:
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
-            return model, tokenizer
+            return
 
-    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
-    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
     if cfg.fsdp:
+        # TODO: do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
+        # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple
+        # processes attempt to write the same file
         if (
             state_dict_type == "SHARDED_STATE_DICT"
             and cfg.fsdp_config.fsdp_state_dict_type == "SHARDED_STATE_DICT"
@@ -252,7 +265,6 @@ def train(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
-
     elif cfg.local_rank == 0:
         if cfg.flash_optimum and BetterTransformer:
             model = BetterTransformer.reverse(model)
@@ -263,40 +275,239 @@ def train(
             )
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+
+def create_model_card(cfg: DictDefault, trainer: Trainer):
+    """
+    Create a model card for the trained model if needed.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The trainer object with model card creation capabilities.
+    """
     if not cfg.hub_model_id:
+        # Guard since create_model_card may fail if dataset_tags is empty list
         try:
             model_card_kwarg = {
                 "model_name": cfg.output_dir.lstrip("./")
                 .encode("utf-8")
                 .decode("utf-8")
             }
-            if cfg.datasets is not None:
+
-                if cfg.rl is not None or cfg.reward_model or cfg.process_reward_model:
+            # We check if we're using a TRL trainer; if so, `dataset_tags` is not consumed.
-                    dataset_tags = [
+            rl = cfg.rl is not None or cfg.reward_model or cfg.process_reward_model
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
+            if cfg.datasets is not None and not rl:
-                    ]
+                dataset_tags = [
-                    dataset_tags = [
+                    d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                        d for d in dataset_tags if not d.startswith("https://")
+                ]
-                    ]
+                dataset_tags = [d for d in dataset_tags if not d.startswith("https://")]
-                    if dataset_tags:
+
-                        # guard as create_model_card may fail if dataset_tags is empty list
+                if dataset_tags:
-                        model_card_kwarg["dataset_name"] = dataset_tags
+                    model_card_kwarg["dataset_tags"] = dataset_tags
-                else:
-                    dataset_tags = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                    dataset_tags = [
-                        d for d in dataset_tags if not d.startswith("https://")
-                    ]
-                    if dataset_tags:
-                        # guard as create_model_card may fail if dataset_tags is empty list
-                        model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
         except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
-        # defensively push to the hub to ensure the model card is updated
+        # Defensively push to the hub to ensure the model card is updated
         trainer.push_to_hub()
 
-    return model, tokenizer
+
+def save_initial_configs(
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    model: PreTrainedModel,
+    peft_config: PeftConfig | None,
+):
+    """
+    Save initial configurations before training.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        tokenizer: The tokenizer to save.
+        model: The model to save configuration for.
+        peft_config: The PEFT configuration to save if applicable.
+    """
+    # Create output_dir if it doesn't already exist
+    output_dir = Path(cfg.output_dir)
+    if not output_dir.is_dir():
+        os.makedirs(cfg.output_dir, exist_ok=True)
+
+    # Pre-save adapter config so it's available to inspect
+    if peft_config:
+        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}...")
+        peft_config.save_pretrained(cfg.output_dir)
+
+    # Pre-save the tokenizer and model configs
+    LOG.info(f"Pre-saving tokenizer to {cfg.output_dir}...")
+    tokenizer.save_pretrained(str(output_dir))
+    if hasattr(model, "config"):
+        LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
+        model.config.save_pretrained(str(output_dir))
+
+
+def setup_model_card(cfg: DictDefault):
+    """
+    Set up the Axolotl badge and add the Axolotl config to the model card if available.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+    """
+    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)"""
+    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
+
+    if getattr(cfg, "axolotl_config_path"):
+        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
+        version = importlib.metadata.version("axolotl")
+        if raw_axolotl_cfg.is_file():
+            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
+
+
+def handle_untrained_tokens_fix(
+    cfg: DictDefault,
+    model: PreTrainedModel,
+    tokenizer: PreTrainedTokenizer,
+    train_dataset: Dataset,
+    safe_serialization: bool,
+):
+    """
+    Apply fixes for untrained tokens if configured.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        model: The model to apply fixes to.
+        tokenizer: The tokenizer for token identification.
+        train_dataset: The training dataset to use.
+        safe_serialization: Whether to use safe serialization when saving.
+    """
+    if not cfg.fix_untrained_tokens:
+        return
+
+    # Check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
+    sig = inspect.signature(fix_untrained_tokens)
+
+    # If the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
+    if "token_ids_to_fix" in sig.parameters and isinstance(
+        cfg.fix_untrained_tokens, list
+    ):
+        fix_untrained_tokens(
+            model,
+            tokenizer,
+            train_dataset,
+            token_ids_to_fix=cfg.fix_untrained_tokens,
+        )
+    else:
+        fix_untrained_tokens(model, tokenizer, train_dataset)
+
+    if cfg.local_rank == 0:
+        model.save_pretrained(
+            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+        )
+
+
+def setup_model_and_trainer(
+    cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> tuple[
+    HFRLTrainerBuilder | HFCausalTrainerBuilder,
+    PeftModel | PreTrainedModel,
+    PreTrainedTokenizer,
+    PeftConfig | None,
+]:
+    """
+    Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
+    trainer setup.
+
+    Args:
+        cfg: The configuration dictionary with training parameters.
+        dataset_meta: Object with training, validation datasets and metadata.
+
+    Returns:
+        Tuple of:
+            - Trainer (Causal or RLHF)
+            - Model
+            - Tokenizer
+            - PEFT config
+    """
+    # Load tokenizer, processor and model
+    model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
+
+    # Set up reference model for RL if needed
+    model_ref = setup_reference_model(cfg, tokenizer)
+
+    # Get datasets from metadata
+    train_dataset = dataset_meta.train_dataset
+    eval_dataset = dataset_meta.eval_dataset
+    total_num_steps = dataset_meta.total_num_steps
+
+    # Set up trainer
+    trainer = setup_trainer(
+        cfg=cfg,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        model=model,
+        tokenizer=tokenizer,
+        processor=processor,
+        total_num_steps=total_num_steps,
+        model_ref=model_ref,
+        peft_config=peft_config,
+    )
+
+    return (
+        trainer,
+        model,
+        tokenizer,
+        peft_config,
+    )
+
+
+def train(
+    cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> tuple[PeftModel | PreTrainedModel, PreTrainedTokenizer, Trainer]:
+    """
+    Train a model on the given dataset.
+
+    Args:
+        cfg: The configuration dictionary with training parameters
+        dataset_meta: Object with training, validation datasets and metadata
+
+    Returns:
+        Tuple of (model, tokenizer) after training
+    """
+    # Setup model, tokenizer, (causal or RLHF) trainer etc.
+    (
+        trainer,
+        model,
+        tokenizer,
+        peft_config,
+    ) = setup_model_and_trainer(cfg, dataset_meta)
+
+    # Determine if we need to resume from a checkpoint
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Configuration for saving
+    safe_serialization = cfg.save_safetensors is True
+
+    # Handle untrained tokens if configured
+    train_dataset = dataset_meta.train_dataset
+    handle_untrained_tokens_fix(
+        cfg, model, tokenizer, train_dataset, safe_serialization
+    )
+
+    # Save initial configs
+    save_initial_configs(cfg, tokenizer, model, peft_config)
+
+    # Set up signal handler for graceful termination
+    setup_signal_handler(cfg, model, safe_serialization)
+
+    # Set up badges and config info for model card
+    setup_model_card(cfg)
+
+    # Execute the training
+    execute_training(cfg, trainer, resume_from_checkpoint)
+
+    # Save the trained model
+    save_trained_model(cfg, trainer, model, safe_serialization)
+
+    # Create model card
+    create_model_card(cfg, trainer)
+
+    return model, tokenizer, trainer

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -64,6 +64,18 @@ class ChatTemplate(str, Enum):
     metharme = "metharme"  # pylint: disable=invalid-name
 
 
+class CustomSupportedOptimizers(str, Enum):
+    """Custom supported optimizers"""
+
+    optimi_adamw = "optimi_adamw"  # pylint: disable=invalid-name
+    ao_adamw_4bit = "ao_adamw_4bit"  # pylint: disable=invalid-name
+    ao_adamw_8bit = "ao_adamw_8bit"  # pylint: disable=invalid-name
+    ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
+    adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
+    lion_pytorch = "lion_pytorch"  # pylint: disable=invalid-name
+    muon = "muon"  # pylint: disable=invalid-name
+
+
 class DeprecatedParameters(BaseModel):
     """configurations that are deprecated"""
 
@@ -494,17 +506,7 @@ class HyperparametersConfig(BaseModel):
     embedding_lr_scale: Optional[float] = None
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
-        Union[
+        Union[OptimizerNames, CustomSupportedOptimizers]
-            OptimizerNames,
-            Literal[
-                "lion_pytorch",
-                "optimi_adamw",
-                "ao_adamw_4bit",
-                "ao_adamw_8bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ],
-        ]
     ] = OptimizerNames.ADAMW_HF
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None,
@@ -518,7 +520,7 @@ class HyperparametersConfig(BaseModel):
     )
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[
-        Union[SchedulerType, Literal["one_cycle"]]
+        Union[SchedulerType, Literal["one_cycle"], Literal["rex"]]
     ] = SchedulerType.COSINE
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
     lr_quadratic_warmup: Optional[bool] = None
@@ -1177,6 +1179,13 @@ class AxolotlInputConfig(
             LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
         return self
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_lr_groups(cls, data):
+        if data.get("lr_groups") and data.get("loraplus_lr_ratio"):
+            raise ValueError("lr_groups and loraplus_lr_ratio cannot be used together.")
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_saves(cls, data):
@@ -1683,7 +1692,7 @@ class AxolotlInputConfig(
 
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
-    """Wrapper to validate GPU capabilities with the config options"""
+    """wrapper to valdiate gpu capabilities with the configured options"""
 
     capabilities: GPUCapabilities
     env_capabilities: EnvCapabilities

src/axolotl/utils/config/models/input/v0_4_1/trl.py

@@ -27,6 +27,7 @@ class TRLConfig(BaseModel):
     vllm_dtype: Optional[str] = "auto"
 
     reward_funcs: Optional[List[str]] = None
+    reward_weights: Optional[List[float]] = None
     num_generations: Optional[int] = None
     log_completions: Optional[bool] = False
 

src/axolotl/utils/models.py

@@ -54,7 +54,6 @@ from axolotl.monkeypatch.multipack import (
     patch_for_multipack,
 )
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
-from axolotl.telemetry.errors import send_errors
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.dict import DictDefault
@@ -166,7 +165,6 @@ def load_model_config(cfg):
     return model_config
 
 
-@send_errors
 def load_tokenizer(cfg):
     model_config = load_model_config(cfg)
     tokenizer_kwargs = {}
@@ -320,7 +318,6 @@ def load_tokenizer(cfg):
     return tokenizer
 
 
-@send_errors
 def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
     processor_kwargs: Dict[str, Any] = {}  # do we actually need this?
 
@@ -1195,17 +1192,18 @@ class ModelLoader:
         return self.model, lora_config
 
 
-@send_errors
 def load_model(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizerBase,
     *,
-    processor: ProcessorMixin = None,
+    processor: ProcessorMixin = None,  # pylint: disable=unused-argument
     inference: bool = False,
     reference_model: bool = False,
-    **kwargs,
+    **kwargs,  # pylint: disable=unused-argument
-) -> Tuple[PreTrainedModel, PeftConfig | None]:
+) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
-    """Load a model for a given configuration and tokenizer"""
+    """
+    Load a model for a given configuration and tokenizer.
+    """
     loader = ModelLoader(
         cfg,
         tokenizer,
@@ -1217,7 +1215,6 @@ def load_model(
     return loader.load_model()
 
 
-@send_errors
 def load_adapter(model, cfg, adapter, inference=False):
     # type: (PreTrainedModel, DictDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
 

src/axolotl/utils/schedulers.py

@@ -6,6 +6,80 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
 
+class RexLR(LRScheduler):
+    """
+    Reflected Exponential (REX) learning rate scheduler.
+
+    - Original implementation: https://github.com/IvanVassi/REX_LR
+    - Original license: Apache 2.0
+    - Based on: https://arxiv.org/abs/2107.04197
+
+    Args:
+        optimizer (torch.optim.Optimizer): The optimizer to schedule the learning rate for.
+        max_lr (float): The maximum learning rate.
+        min_lr (float): The minimum learning rate.
+        total_steps (int): The total number of training steps.
+        num_warmup_steps (int): The number of warmup steps.
+        last_step (int): The index of last step.
+    """
+
+    def __init__(
+        self, optimizer, max_lr, min_lr, total_steps=0, num_warmup_steps=0, last_step=0
+    ):
+        if min_lr > max_lr:
+            raise ValueError(
+                f'Value of "min_lr" should be less than value of "max_lr". Got min_lr={min_lr} and max_lr={max_lr}'
+            )
+        if num_warmup_steps > total_steps:
+            raise ValueError(
+                f"num_warmup_steps ({num_warmup_steps}) must be less than or equal to total_steps ({total_steps})."
+            )
+
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.total_steps = total_steps
+        self.num_warmup_steps = num_warmup_steps
+        self.last_step = last_step - 1
+
+        # Ensure each parameter group has an "initial_lr" key to avoid issues when resuming.
+        for group in optimizer.param_groups:
+            group.setdefault("initial_lr", group["lr"])
+
+        # Pass self.last_step as last_epoch to the parent.
+        super().__init__(optimizer, last_epoch=self.last_step)
+
+    @property
+    def last_step(self):
+        return self.last_epoch
+
+    @last_step.setter
+    def last_step(self, value):
+        self.last_epoch = value
+
+    def get_lr(self):
+        # Warmup phase: if defined, increase lr linearly from 0 to max_lr.
+        if 1 <= self.last_step <= self.num_warmup_steps:
+            return [
+                base_lr * self.last_step / self.num_warmup_steps
+                for base_lr in self.base_lrs
+            ]
+
+        # Post-warmup phase: adjust step relative to the end of warmup.
+        step_after = self.last_step - self.num_warmup_steps
+        remaining_steps = self.total_steps - self.num_warmup_steps
+
+        # Avoid LR spiking
+        if step_after >= remaining_steps or step_after == -1 or remaining_steps <= 0:
+            return [self.min_lr for _ in self.base_lrs]
+
+        mod_iter = step_after % remaining_steps
+        z = (remaining_steps - mod_iter) / remaining_steps
+        rex_factor = self.min_lr / self.max_lr + (1.0 - self.min_lr / self.max_lr) * (
+            z / (0.1 + 0.9 * z)
+        )
+        return [base_lr * rex_factor for base_lr in self.base_lrs]
+
+
 class InterpolatingLogScheduler(LRScheduler):
     """
     A scheduler that interpolates learning rates in a logarithmic fashion

src/axolotl/utils/trainer.py

@@ -574,14 +574,40 @@ def prepare_opinionated_env(cfg):
 
 
 def setup_trainer(
-    cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
+    cfg,
+    train_dataset,
+    eval_dataset,
+    model,
+    tokenizer,
+    processor,
+    total_num_steps,
+    model_ref=None,
+    peft_config=None,
 ):
+    """
+    Helper method for instantiating and building a (causal or RLHF) trainer.
+
+    Args:
+        cfg: Axolotl config object containing training parameters.
+        train_dataset: Dataset to use for training.
+        eval_dataset: Dataset to use for evaluation.
+        model: The model to train.
+        tokenizer: Tokenizer for processing text input.
+        processor: Processor for data preparation.
+        total_num_steps: The total number of training steps.
+        model_ref: Optional reference model for RLHF training. Default is None.
+        peft_config: Optional PEFT (Parameter-Efficient Fine-Tuning) configuration. Default is None.
+
+    Returns:
+        A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
+            on the provided parameters.
+    """
     if cfg.rl:
-        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
+        trainer_builder = HFRLTrainerBuilder(cfg, model, tokenizer, processor)
-        trainer_builder.model_ref = model[1]
+        trainer_builder.model_ref = model_ref
-        trainer_builder.peft_config = model[2]
+        trainer_builder.peft_config = peft_config
     else:
-        trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer, processor)
+        trainer_builder = HFCausalTrainerBuilder(cfg, model, tokenizer, processor)
 
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset

tests/cli/test_cli_train.py

@@ -28,7 +28,7 @@ class TestTrainCommand(BaseCliTest):
         config_path.write_text(valid_test_config)
 
         with patch("axolotl.cli.train.train") as mock_train:
-            mock_train.return_value = (MagicMock(), MagicMock())
+            mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
 
             result = cli_runner.invoke(
                 cli,
@@ -48,7 +48,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = self._test_cli_overrides(tmp_path, valid_test_config)
 
         with patch("axolotl.cli.train.train") as mock_train:
-            mock_train.return_value = (MagicMock(), MagicMock())
+            mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
 
             result = cli_runner.invoke(
                 cli,

tests/conftest.py

@@ -1,5 +1,6 @@
-"""Shared pytest fixtures"""
+"""
-
+shared pytest fixtures
+"""
 import functools
 import importlib
 import shutil
@@ -170,9 +171,3 @@ def cleanup_monkeypatches():
             module_globals = module_name_tuple[1]
             for module_global in module_globals:
                 globals().pop(module_global, None)
-
-
-@pytest.fixture(autouse=True)
-def disable_telemetry(monkeypatch):
-    monkeypatch.setenv("AXOLOTL_DO_NOT_TRACK", "1")
-    yield

tests/e2e/test_mixtral.py

@@ -75,7 +75,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -131,7 +131,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -190,7 +190,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -249,7 +249,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32

tests/e2e/test_optimizers.py

@@ -65,8 +65,9 @@ class TestCustomOptimizers(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+        assert trainer.optimizer.optimizer.__class__.__name__ == "AdamW"
 
     @with_temp_dir
     @require_torch_2_5_1
@@ -111,8 +112,57 @@ class TestCustomOptimizers(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+        assert "ADOPT" in trainer.optimizer.optimizer.__class__.__name__
+
+    @with_temp_dir
+    @require_torch_2_5_1
+    def test_muon(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "muon",
+                "lr_scheduler": "cosine",
+                "weight_decay": 0.01,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+        assert "Muon" in trainer.optimizer.optimizer.__class__.__name__
 
     @with_temp_dir
     def test_fft_schedule_free_adamw(self, temp_dir):

tests/e2e/test_schedulers.py

@@ -0,0 +1,71 @@
+"""
+E2E tests for custom schedulers using Llama
+"""
+
+import logging
+import os
+import unittest
+
+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, validate_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestCustomSchedulers(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_rex_scheduler(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_hf",
+                "max_steps": 20,
+                "lr_scheduler": "rex",
+                "warmup_steps": 5,
+                "cosine_min_lr_ratio": 0.05,
+            }
+        )
+
+        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)
+        check_model_output_exists(temp_dir, cfg)

tests/telemetry/conftest.py

@@ -1,9 +0,0 @@
-"""Shared pytest fixtures for telemetry tests."""
-
-import pytest
-
-
-@pytest.fixture(autouse=True)
-def disable_telemetry(monkeypatch):
-    monkeypatch.delenv("AXOLOTL_DO_NOT_TRACK", raising=False)
-    yield

tests/telemetry/test_callbacks.py

@@ -1,372 +0,0 @@
-"""Tests for telemetry callback module."""
-# pylint: disable=redefined-outer-name
-
-import time
-from unittest.mock import MagicMock, patch
-
-import pytest
-from transformers import TrainerControl, TrainerState, TrainingArguments
-
-from axolotl.telemetry.callbacks import TIME_SINCE_LAST, TelemetryCallback
-
-
-def calc_expected_metrics(step, last_step, current_time, last_time, start_time=900.0):
-    """Calculate expected metrics values for tests"""
-    time_diff = current_time - last_time
-    step_diff = step - last_step
-    return {
-        "steps_per_second": step_diff / time_diff
-        if time_diff > 0 and step_diff > 0
-        else 0,
-        "time_since_last_report": time_diff,
-        "elapsed_time": current_time - start_time,
-    }
-
-
-@pytest.fixture
-def mock_time():
-    """Mock time.time() to have predictable values in tests"""
-    with patch("axolotl.telemetry.callbacks.time") as mock_time:
-        mock_time.time.return_value = 1000.0
-        yield mock_time
-
-
-@pytest.fixture
-def mock_telemetry_manager():
-    """Create a mock TelemetryManager"""
-    with patch("axolotl.telemetry.callbacks.TelemetryManager") as mock_manager_class:
-        mock_manager = MagicMock()
-        mock_manager_class.get_instance.return_value = mock_manager
-        yield mock_manager
-
-
-@pytest.fixture
-def mock_runtime_metrics_tracker():
-    """Create a mock RuntimeMetricsTracker"""
-    with patch(
-        "axolotl.telemetry.callbacks.RuntimeMetricsTracker"
-    ) as mock_tracker_class:
-        mock_tracker = MagicMock()
-        # Set up metrics property on the tracker
-        mock_metrics = MagicMock()
-        mock_metrics.to_dict.return_value = {
-            "total_steps": 100,
-            "peak_cpu_memory_bytes": 1024,
-        }
-        mock_tracker.metrics = mock_metrics
-
-        # Make the constructor return our mock
-        mock_tracker_class.return_value = mock_tracker
-        yield mock_tracker
-
-
-@pytest.fixture
-def training_args():
-    """Create a minimal TrainingArguments instance"""
-    return TrainingArguments(output_dir="./output")
-
-
-@pytest.fixture
-def trainer_state():
-    """Create a mock TrainerState"""
-    state = MagicMock(spec=TrainerState)
-    state.global_step = 10
-    state.epoch = 0.5  # halfway through first epoch
-    state.log_history = [{"loss": 2.5, "learning_rate": 5e-5}]
-    return state
-
-
-@pytest.fixture
-def trainer_control():
-    """Create a mock TrainerControl"""
-    return MagicMock(spec=TrainerControl)
-
-
-# pylint: disable=unused-argument
-@pytest.fixture
-def callback(mock_telemetry_manager, mock_runtime_metrics_tracker):
-    """Create a TelemetryCallback instance with mocked dependencies"""
-    return TelemetryCallback()
-
-
-class TestTelemetryCallback:
-    """Tests for the TelemetryCallback class."""
-
-    def test_initialization(self, callback, mock_runtime_metrics_tracker):
-        """Test callback initialization."""
-        assert callback.current_epoch == -1
-        assert callback.tracker == mock_runtime_metrics_tracker
-        assert callback.last_report_step == 0
-        assert hasattr(callback, "start_time")
-        assert hasattr(callback, "last_report_time")
-        assert callback.report_interval_steps == 100
-
-    def test_on_train_begin(
-        self,
-        callback,
-        mock_telemetry_manager,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_train_begin sends expected event."""
-        callback.on_train_begin(training_args, trainer_state, trainer_control)
-
-        mock_telemetry_manager.send_event.assert_called_once_with(
-            event_type="train-started"
-        )
-
-    def test_on_train_end(
-        self,
-        callback,
-        mock_telemetry_manager,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_train_end sends expected event with metrics."""
-        callback.on_train_end(training_args, trainer_state, trainer_control)
-
-        mock_telemetry_manager.send_event.assert_called_once()
-        call_args = mock_telemetry_manager.send_event.call_args[1]
-
-        assert call_args["event_type"] == "train-ended"
-        assert "loss" in call_args["properties"]
-        assert call_args["properties"]["loss"] == 2.5
-        assert "learning_rate" in call_args["properties"]
-        assert call_args["properties"]["learning_rate"] == 5e-5
-
-        # Check that metrics from RuntimeMetricsTracker are included
-        assert "total_steps" in call_args["properties"]
-        assert call_args["properties"]["total_steps"] == 100
-        assert "peak_cpu_memory_bytes" in call_args["properties"]
-        assert call_args["properties"]["peak_cpu_memory_bytes"] == 1024
-
-    def test_on_epoch_begin(
-        self,
-        callback,
-        mock_runtime_metrics_tracker,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_epoch_begin updates epoch counter and calls tracker."""
-        initial_epoch = callback.current_epoch
-
-        callback.on_epoch_begin(training_args, trainer_state, trainer_control)
-
-        assert callback.current_epoch == initial_epoch + 1
-        mock_runtime_metrics_tracker.start_epoch.assert_called_once_with(
-            initial_epoch + 1
-        )
-
-    def test_on_epoch_end(
-        self,
-        callback,
-        mock_runtime_metrics_tracker,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_epoch_end calls tracker."""
-        # Set current epoch
-        callback.current_epoch = 2
-
-        callback.on_epoch_end(training_args, trainer_state, trainer_control)
-
-        mock_runtime_metrics_tracker.end_epoch.assert_called_once_with(2)
-
-    def test_on_step_end_no_report(
-        self,
-        callback,
-        mock_telemetry_manager,
-        mock_runtime_metrics_tracker,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_step_end updates tracker but doesn't report if criteria not met."""
-        # Set up state to avoid reporting
-        trainer_state.global_step = 42  # Not divisible by report_interval_steps
-        callback.last_report_step = 41  # Just 1 step since last report
-        callback.last_report_time = time.time()  # Just now
-
-        callback.on_step_end(training_args, trainer_state, trainer_control)
-
-        # Should update tracker
-        mock_runtime_metrics_tracker.update_step.assert_called_once_with(42)
-
-        # Should not send telemetry
-        mock_telemetry_manager.send_event.assert_not_called()
-
-        # Should not update last report time/step
-        assert callback.last_report_step == 41
-
-    def test_on_step_end_report_interval_steps(
-        self,
-        callback,
-        mock_telemetry_manager,
-        mock_runtime_metrics_tracker,
-        mock_time,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_step_end reports when step interval is reached."""
-        # Set up state with clear values
-        current_step = 100  # Exactly matches report_interval_steps
-        last_step = 0
-        start_time = 900.0
-        current_time = 1000.0
-        time_diff = current_time - start_time  # 100 seconds
-
-        # Configure state and callback
-        trainer_state.global_step = current_step
-        callback.report_interval_steps = 100
-        callback.last_report_step = last_step
-        callback.start_time = start_time
-        callback.last_report_time = start_time
-
-        # Mock time.time() to return consistent values
-        mock_time.time.return_value = current_time
-
-        callback.on_step_end(training_args, trainer_state, trainer_control)
-
-        # Should update tracker
-        mock_runtime_metrics_tracker.update_step.assert_called_once_with(current_step)
-        mock_runtime_metrics_tracker.update_memory_metrics.assert_called_once()
-
-        # Should send telemetry
-        mock_telemetry_manager.send_event.assert_called_once()
-        call_args = mock_telemetry_manager.send_event.call_args[1]
-        assert call_args["event_type"] == "train-progress"
-
-        # Properties should include expected values
-        props = call_args["properties"]
-        assert props["step"] == current_step
-        assert props["elapsed_time"] == time_diff  # 1000 - 900 = 100
-        assert props["time_since_last_report"] == time_diff  # 1000 - 900 = 100
-        assert props["steps_per_second"] == 1.0  # 100 steps / 100 seconds
-
-        # Should update last report time/step
-        assert callback.last_report_step == current_step
-        assert callback.last_report_time == current_time
-
-    def test_on_step_end_report_time_elapsed(
-        self,
-        callback,
-        mock_telemetry_manager,
-        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
-        mock_time,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_step_end reports when enough time has elapsed."""
-        # Set up state with clear values
-        current_step = 120
-        last_step = 10
-        start_time = 900.0
-        current_time = 1000.0
-        time_diff = TIME_SINCE_LAST + 1  # Just over the threshold
-
-        # Configure state and callback
-        trainer_state.global_step = current_step
-        callback.report_interval_steps = 100
-        callback.last_report_step = last_step
-        callback.start_time = start_time
-        callback.last_report_time = current_time - time_diff
-
-        # Mock time.time() to return consistent values
-        mock_time.time.return_value = current_time
-
-        callback.on_step_end(training_args, trainer_state, trainer_control)
-
-        # Should send telemetry
-        mock_telemetry_manager.send_event.assert_called_once()
-
-        # Properties should include expected values
-        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
-        expected_metrics = calc_expected_metrics(
-            current_step, last_step, current_time, current_time - time_diff, start_time
-        )
-        assert props["steps_per_second"] == expected_metrics["steps_per_second"]
-        assert (
-            props["time_since_last_report"]
-            == expected_metrics["time_since_last_report"]
-        )
-
-    def test_on_step_end_first_step(
-        self,
-        callback,
-        mock_telemetry_manager,
-        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
-        mock_time,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test on_step_end always reports on first step."""
-        # Set up state with clear values
-        current_step = 1  # First step
-        last_step = 0
-        start_time = 900.0
-        current_time = 1000.0
-        last_report_time = 999.0  # Just 1 second ago
-
-        # Configure state and callback
-        trainer_state.global_step = current_step
-        callback.report_interval_steps = 100
-        callback.last_report_step = last_step
-        callback.start_time = start_time
-        callback.last_report_time = last_report_time
-
-        # Mock time.time() to return consistent values
-        mock_time.time.return_value = current_time
-
-        callback.on_step_end(training_args, trainer_state, trainer_control)
-
-        # Should send telemetry even though not much time has passed
-        mock_telemetry_manager.send_event.assert_called_once()
-
-        # Properties should include expected values for first step
-        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
-        assert props["step"] == current_step
-        expected_metrics = calc_expected_metrics(
-            current_step, last_step, current_time, last_report_time, start_time
-        )
-        assert props["steps_per_second"] == expected_metrics["steps_per_second"]
-
-    def test_log_history_empty(
-        self,
-        callback,
-        mock_telemetry_manager,
-        mock_runtime_metrics_tracker,  # pylint: disable=unused-argument
-        mock_time,
-        training_args,
-        trainer_state,
-        trainer_control,
-    ):
-        """Test handling of empty log history."""
-        # Set up state with clear values
-        current_step = 1
-        start_time = 900.0
-        current_time = 1000.0
-
-        # Configure state and callback
-        trainer_state.global_step = current_step
-        trainer_state.log_history = []
-        callback.start_time = start_time
-
-        # Mock time.time() to return consistent values
-        mock_time.time.return_value = current_time
-
-        callback.on_step_end(training_args, trainer_state, trainer_control)
-
-        # Should still send telemetry
-        mock_telemetry_manager.send_event.assert_called_once()
-
-        # Properties should have default values for missing log data
-        props = mock_telemetry_manager.send_event.call_args[1]["properties"]
-        assert props["loss"] == 0
-        assert props["learning_rate"] == 0

tests/telemetry/test_errors.py

@@ -1,340 +0,0 @@
-"""Tests for telemetry error utilities"""
-# pylint: disable=redefined-outer-name
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from axolotl.telemetry.errors import sanitize_stack_trace, send_errors
-
-
-@pytest.fixture(autouse=True)
-def reset_error_flag(monkeypatch):
-    """Reset ERROR_HANDLED flag using monkeypatch"""
-    import axolotl.telemetry.errors
-
-    monkeypatch.setattr(axolotl.telemetry.errors, "ERROR_HANDLED", False)
-    yield
-    monkeypatch.setattr(axolotl.telemetry.errors, "ERROR_HANDLED", False)
-
-
-@pytest.fixture
-def example_stack_trace():
-    """Provide a sample stack trace with mixed paths"""
-    return """Traceback (most recent call last):
-  File "/home/user/.local/lib/python3.9/site-packages/axolotl/cli/train.py", line 83, in main
-    trainer = get_trainer(cfg)
-  File "/home/user/.local/lib/python3.9/site-packages/axolotl/train.py", line 214, in get_trainer
-    model = get_model(cfg, tokenizer)
-  File "/home/user/.local/lib/python3.9/site-packages/axolotl/utils/models.py", line 120, in get_model
-    raise ValueError("Model path not found")
-ValueError: Model path not found
-"""
-
-
-@pytest.fixture
-def windows_stack_trace():
-    """Provide a sample stack trace with Windows paths"""
-    return """Traceback (most recent call last):
-  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\axolotl\\cli\\train.py", line 83, in main
-    trainer = get_trainer(cfg)
-  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\axolotl\\train.py", line 214, in get_trainer
-    model = get_model(cfg, tokenizer)
-  File "C:\\Users\\name\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\transformers\\models\\auto\\modeling_auto.py", line 482, in from_pretrained
-    raise ValueError(f"Unrecognized configuration class {config.__class__}")
-ValueError: Unrecognized configuration class <class 'transformers.models.llama.configuration_llama.LlamaConfig'>
-"""
-
-
-@pytest.fixture
-def mixed_stack_trace():
-    """Provide a sample stack trace with both axolotl and non-axolotl paths"""
-    return """Traceback (most recent call last):
-  File "/home/user/.local/lib/python3.9/site-packages/axolotl/cli/train.py", line 83, in main
-    trainer = get_trainer(cfg)
-  File "/home/user/.local/lib/python3.9/site-packages/transformers/trainer.py", line 520, in train
-    self._inner_training_loop()
-  File "/home/user/.local/lib/python3.9/site-packages/axolotl/utils/trainer.py", line 75, in _inner_training_loop
-    super()._inner_training_loop()
-  File "/home/user/.local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
-    data = self._next_data()
-RuntimeError: CUDA out of memory
-"""
-
-
-@pytest.fixture
-def venv_stack_trace():
-    """Provide a sample stack trace with virtual environment paths"""
-    return """Traceback (most recent call last):
-  File "/home/user/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1729, in train
-    self._inner_training_loop()
-  File "/home/user/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2013, in _inner_training_loop
-    self.accelerator.backward(loss)
-  File "/home/user/venv/lib/python3.9/site-packages/accelerate/accelerator.py", line 1851, in backward
-    self.scaler.scale(loss).backward(**kwargs)
-  File "/home/user/venv/lib/python3.9/site-packages/torch/_tensor.py", line 487, in backward
-    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
-RuntimeError: CUDA out of memory
-"""
-
-
-@pytest.fixture
-def dist_packages_stack_trace():
-    """Provide a sample stack trace with dist-packages paths"""
-    return """Traceback (most recent call last):
-  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 631, in __next__
-    data = self._next_data()
-  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 675, in _next_data
-    data = self._dataset_fetcher.fetch(index)
-  File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
-    data = [self.dataset[idx] for idx in possibly_batched_index]
-  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_dataset.py", line 2808, in __getitem__
-    raise IndexError(f"Index {key} out of range for dataset of length {len(self)}.")
-IndexError: Index 10000 out of range for dataset of length 9832.
-"""
-
-
-@pytest.fixture
-def project_stack_trace():
-    """Provide a sample stack trace from a project directory (not a virtual env)"""
-    return """Traceback (most recent call last):
-  File "/home/user/projects/myproject/run.py", line 25, in <module>
-    main()
-  File "/home/user/projects/myproject/src/cli.py", line 45, in main
-    app.run()
-  File "/home/user/projects/myproject/src/app.py", line 102, in run
-    raise ValueError("Configuration missing")
-ValueError: Configuration missing
-"""
-
-
-def test_sanitize_stack_trace(example_stack_trace):
-    """Test that sanitize_stack_trace properly preserves axolotl paths"""
-    sanitized = sanitize_stack_trace(example_stack_trace)
-
-    # Check that personal paths are removed
-    assert "/home/user" not in sanitized
-    assert ".local/lib/python3.9" not in sanitized
-
-    # Check that site-packages is preserved
-    assert "site-packages/axolotl/cli/train.py" in sanitized
-    assert "site-packages/axolotl/train.py" in sanitized
-    assert "site-packages/axolotl/utils/models.py" in sanitized
-
-    # Check that error message is preserved
-    assert "ValueError: Model path not found" in sanitized
-
-
-def test_sanitize_windows_paths(windows_stack_trace):
-    """Test that sanitize_stack_trace handles Windows paths"""
-    sanitized = sanitize_stack_trace(windows_stack_trace)
-
-    # Check that personal paths are removed
-    assert "C:\\Users\\name" not in sanitized
-    assert "AppData\\Local\\Programs\\Python" not in sanitized
-
-    # Check that both axolotl and transformers packages are preserved
-    assert (
-        "site-packages\\axolotl\\cli\\train.py" in sanitized
-        or "site-packages/axolotl/cli/train.py" in sanitized
-    )
-    assert (
-        "site-packages\\axolotl\\train.py" in sanitized
-        or "site-packages/axolotl/train.py" in sanitized
-    )
-    assert (
-        "site-packages\\transformers\\models\\auto\\modeling_auto.py" in sanitized
-        or "site-packages/transformers/models/auto/modeling_auto.py" in sanitized
-    )
-
-    # Check that error message is preserved
-    assert "ValueError: Unrecognized configuration class" in sanitized
-
-
-def test_sanitize_mixed_paths(mixed_stack_trace):
-    """Test that sanitize_stack_trace preserves all package paths"""
-    sanitized = sanitize_stack_trace(mixed_stack_trace)
-
-    # Check that all package paths are preserved
-    assert "site-packages/axolotl/cli/train.py" in sanitized
-    assert "site-packages/transformers/trainer.py" in sanitized
-    assert "site-packages/axolotl/utils/trainer.py" in sanitized
-    assert "site-packages/torch/utils/data/dataloader.py" in sanitized
-
-    # Check that error message is preserved
-    assert "RuntimeError: CUDA out of memory" in sanitized
-
-
-def test_sanitize_venv_paths(venv_stack_trace):
-    """Test that sanitize_stack_trace preserves virtual environment package paths"""
-    sanitized = sanitize_stack_trace(venv_stack_trace)
-
-    # Check that personal paths are removed
-    assert "/home/user/venv" not in sanitized
-
-    # Check that all package paths are preserved
-    assert "site-packages/transformers/trainer.py" in sanitized
-    assert "site-packages/accelerate/accelerator.py" in sanitized
-    assert "site-packages/torch/_tensor.py" in sanitized
-
-    # Check that error message is preserved
-    assert "RuntimeError: CUDA out of memory" in sanitized
-
-
-def test_sanitize_dist_packages(dist_packages_stack_trace):
-    """Test that sanitize_stack_trace preserves dist-packages paths"""
-    sanitized = sanitize_stack_trace(dist_packages_stack_trace)
-
-    # Check that system paths are removed
-    assert "/usr/local/lib/python3.8" not in sanitized
-
-    # Check that all package paths are preserved
-    assert "dist-packages/torch/utils/data/dataloader.py" in sanitized
-    assert "dist-packages/torch/utils/data/_utils/fetch.py" in sanitized
-    assert "dist-packages/datasets/arrow_dataset.py" in sanitized
-
-    # Check that error message is preserved
-    assert (
-        "IndexError: Index 10000 out of range for dataset of length 9832." in sanitized
-    )
-
-
-def test_sanitize_project_paths(project_stack_trace):
-    """Test handling of project paths (non-virtual env)"""
-    sanitized = sanitize_stack_trace(project_stack_trace)
-
-    # Check that personal paths are removed
-    assert "/home/user/projects" not in sanitized
-
-    # For non-package paths, we should at least preserve the filename
-    assert "run.py" in sanitized
-    assert "cli.py" in sanitized
-    assert "app.py" in sanitized
-
-    # Check that error message is preserved
-    assert "ValueError: Configuration missing" in sanitized
-
-
-@pytest.fixture
-def mock_telemetry_manager():
-    """Create a mock TelemetryManager"""
-    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
-        mock_manager = MagicMock()
-        mock_manager.enabled = True
-        mock_manager_class.get_instance.return_value = mock_manager
-        yield mock_manager
-
-
-def test_send_errors_successful_execution(mock_telemetry_manager):
-    """Test that send_errors doesn't send telemetry for successful function execution"""
-
-    @send_errors
-    def test_func():
-        return "success"
-
-    result = test_func()
-    assert result == "success"
-    mock_telemetry_manager.send_event.assert_not_called()
-
-
-def test_send_errors_with_exception(mock_telemetry_manager):
-    """Test that send_errors sends telemetry when an exception occurs"""
-    test_error = ValueError("Test error")
-
-    @send_errors
-    def test_func():
-        raise test_error
-
-    with pytest.raises(ValueError) as excinfo:
-        test_func()
-
-    assert excinfo.value == test_error
-    mock_telemetry_manager.send_event.assert_called_once()
-
-    # Check that the error info was passed correctly
-    call_args = mock_telemetry_manager.send_event.call_args[1]
-    assert "test_func-errored" in call_args["event_type"]
-    assert "Test error" in call_args["properties"]["exception"]
-    assert "stack_trace" in call_args["properties"]
-
-
-def test_send_errors_nested_calls(mock_telemetry_manager):
-    """Test that send_errors only sends telemetry once for nested decorated functions"""
-
-    @send_errors
-    def inner_func():
-        raise ValueError("Inner error")
-
-    @send_errors
-    def outer_func():
-        return inner_func()
-
-    with pytest.raises(ValueError):
-        outer_func()
-
-    # Telemetry should be sent only once for the inner function
-    assert mock_telemetry_manager.send_event.call_count == 1
-    call_args = mock_telemetry_manager.send_event.call_args[1]
-    assert "inner_func-error" in call_args["event_type"]
-
-
-def test_send_errors_telemetry_disable():
-    """Test that send_errors doesn't attempt to send telemetry when disabled"""
-
-    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
-        mock_manager = MagicMock()
-        mock_manager.enabled = False
-        mock_manager_class.get_instance.return_value = mock_manager
-
-        @send_errors
-        def test_func():
-            raise ValueError("Test error")
-
-        with pytest.raises(ValueError):
-            test_func()
-
-        mock_manager.send_event.assert_not_called()
-
-
-def test_error_handled_reset():
-    """Test that ERROR_HANDLED flag is properly reset"""
-    with patch("axolotl.telemetry.errors.TelemetryManager") as mock_manager_class:
-        # Create and configure the mock manager
-        mock_manager = MagicMock()
-        mock_manager.enabled = True
-        mock_manager_class.get_instance.return_value = mock_manager
-
-        from axolotl.telemetry.errors import ERROR_HANDLED
-
-        @send_errors
-        def test_func():
-            raise ValueError("Test error")
-
-        assert not ERROR_HANDLED
-
-        with pytest.raises(ValueError):
-            test_func()
-
-        from axolotl.telemetry.errors import ERROR_HANDLED
-
-        assert ERROR_HANDLED
-
-
-def test_module_path_resolution(mock_telemetry_manager):
-    """Test that the module path is correctly resolved for the event type"""
-    import inspect
-
-    current_module = inspect.getmodule(test_module_path_resolution).__name__
-
-    @send_errors
-    def test_func():
-        raise ValueError("Test error")
-
-    with pytest.raises(ValueError):
-        test_func()
-
-    assert mock_telemetry_manager.send_event.called
-    event_type = mock_telemetry_manager.send_event.call_args[1]["event_type"]
-
-    expected_event_type = f"{current_module}.test_func-errored"
-    assert expected_event_type == event_type

tests/telemetry/test_manager.py

@@ -1,245 +0,0 @@
-"""Tests for TelemetryManager class and utilities"""
-# pylint: disable=redefined-outer-name,protected-access
-
-import os
-from unittest.mock import patch
-
-import pytest
-import yaml
-
-from axolotl.telemetry.manager import TelemetryManager
-
-
-@pytest.fixture
-def mock_whitelist(tmp_path):
-    """Create a temporary whitelist file for testing"""
-    whitelist_content = {
-        "organizations": ["meta-llama", "mistralai"],
-    }
-    whitelist_file = tmp_path / "whitelist.yaml"
-    with open(whitelist_file, "w", encoding="utf-8") as f:
-        yaml.dump(whitelist_content, f)
-
-    return str(whitelist_file)
-
-
-@pytest.fixture
-def telemetry_manager_class():
-    """Reset the TelemetryManager singleton between tests"""
-    original_instance = TelemetryManager._instance
-    original_initialized = TelemetryManager._initialized
-    TelemetryManager._instance = None
-    TelemetryManager._initialized = False
-    yield TelemetryManager
-    TelemetryManager._instance = original_instance
-    TelemetryManager._initialized = original_initialized
-
-
-@pytest.fixture
-def manager(telemetry_manager_class, mock_whitelist):
-    """Create a TelemetryManager instance with mocked dependencies"""
-    with patch("posthog.capture"), patch("posthog.flush"), patch("time.sleep"), patch(
-        "axolotl.telemetry.manager.WHITELIST_PATH", mock_whitelist
-    ), patch.dict(os.environ, {"RANK": "0"}):
-        manager = telemetry_manager_class()
-        # Manually enable for most tests
-        manager.enabled = True
-        return manager
-
-
-def test_singleton_instance(telemetry_manager_class):
-    """Test that TelemetryManager is a singleton"""
-    with patch("posthog.capture"), patch("time.sleep"), patch.dict(
-        os.environ, {"RANK": "0"}
-    ):
-        first = telemetry_manager_class()
-        second = telemetry_manager_class()
-        assert first is second
-        assert telemetry_manager_class.get_instance() is first
-
-
-def test_telemetry_disabled_with_axolotl_do_not_track(telemetry_manager_class):
-    """Test that telemetry is disabled when AXOLOTL_DO_NOT_TRACK=1"""
-    with patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "1", "RANK": "0"}):
-        manager = telemetry_manager_class()
-        assert not manager.enabled
-
-
-def test_telemetry_disabled_with_do_not_track(telemetry_manager_class):
-    """Test that telemetry is disabled when DO_NOT_TRACK=1"""
-    with patch.dict(os.environ, {"DO_NOT_TRACK": "1", "RANK": "0"}):
-        manager = telemetry_manager_class()
-        assert not manager.enabled
-
-
-def test_telemetry_disabled_for_non_main_process(telemetry_manager_class):
-    """Test that telemetry is disabled for non-main processes"""
-    with patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0", "RANK": "1"}):
-        manager = telemetry_manager_class()
-        assert not manager.enabled
-
-
-def test_telemetry_enabled_by_default(telemetry_manager_class):
-    """Test that telemetry is enabled by default"""
-    with patch.dict(os.environ, {"RANK": "0"}, clear=True), patch("time.sleep"), patch(
-        "logging.Logger.warning"
-    ):
-        manager = telemetry_manager_class()
-        assert manager.enabled
-        assert not manager.explicit_enable
-
-
-def test_explicit_enable_disables_warning(telemetry_manager_class):
-    """Test that explicit enabling prevents warning"""
-    with patch.dict(os.environ, {"AXOLOTL_DO_NOT_TRACK": "0", "RANK": "0"}), patch(
-        "logging.Logger.warning"
-    ) as mock_warning, patch("time.sleep"):
-        manager = telemetry_manager_class()
-        assert manager.enabled
-        assert manager.explicit_enable
-        for call in mock_warning.call_args_list:
-            assert "Telemetry is enabled" not in str(call)
-
-
-def test_warning_displayed_for_implicit_enable(telemetry_manager_class):
-    """Test that warning is displayed when telemetry is implicitly enabled"""
-    with patch.dict(os.environ, {"RANK": "0"}, clear=True), patch(
-        "logging.Logger.warning"
-    ) as mock_warning, patch("time.sleep"):
-        manager = telemetry_manager_class()
-        assert manager.enabled
-        assert not manager.explicit_enable
-        warning_displayed = False
-        for call in mock_warning.call_args_list:
-            if "Telemetry is enabled" in str(call):
-                warning_displayed = True
-                break
-        assert warning_displayed
-
-
-def test_is_whitelisted(manager, mock_whitelist):
-    """Test org whitelist functionality"""
-    with patch("axolotl.telemetry.manager.WHITELIST_PATH", mock_whitelist):
-        # Should match organizations from the mock whitelist
-        assert manager._is_whitelisted("meta-llama/llama-7b")
-        assert manager._is_whitelisted("mistralai/mistral-7b-instruct")
-        # Should not match
-        assert not manager._is_whitelisted("unknown/model")
-        # Should handle case insensitively
-        assert manager._is_whitelisted("META-LLAMA/Llama-7B")
-        # Should handle empty input
-        assert not manager._is_whitelisted("")
-        assert not manager._is_whitelisted(None)
-
-
-def test_system_info_collection(manager):
-    """Test system information collection"""
-    system_info = manager._get_system_info()
-
-    # Check essential keys
-    assert "os" in system_info
-    assert "python_version" in system_info
-    assert "torch_version" in system_info
-    assert "transformers_version" in system_info
-    assert "axolotl_version" in system_info
-    assert "cpu_count" in system_info
-    assert "memory_total" in system_info
-    assert "accelerator_count" in system_info
-
-
-def test_send_event(manager):
-    """Test basic event sending"""
-    with patch("posthog.capture") as mock_capture:
-        # Test with clean properties (no PII)
-        manager.send_event("test_event", {"key": "value"})
-        assert mock_capture.called
-        assert mock_capture.call_args[1]["event"] == "test_event"
-        assert mock_capture.call_args[1]["properties"] == {"key": "value"}
-        assert mock_capture.call_args[1]["distinct_id"] == manager.run_id
-
-        # Test with default properties (None)
-        mock_capture.reset_mock()
-        manager.send_event("simple_event")
-        assert mock_capture.called
-        assert mock_capture.call_args[1]["properties"] == {}
-
-
-def test_send_system_info(manager):
-    """Test sending system info"""
-    with patch("posthog.capture") as mock_capture:
-        manager.send_system_info()
-        assert mock_capture.called
-        assert mock_capture.call_args[1]["event"] == "system-info"
-        assert mock_capture.call_args[1]["properties"] == manager.system_info
-
-
-def test_redacted_properties(manager):
-    """Test path redaction in send_event method"""
-    with patch("posthog.capture") as mock_capture:
-        # Test with properties containing various paths and non-paths
-        test_properties = {
-            "filepath": "/home/user/sensitive/data.txt",
-            "windows_path": "C:\\Users\\name\\Documents\\project\\file.py",
-            "output_dir": "/var/lib/data",
-            "path_to_model": "models/llama/7b",
-            "message": "Training started",  # Should not be redacted
-            "metrics": {"loss": 0.5, "accuracy": 0.95},  # Should not be redacted
-            "base_model": "models/local_model",
-            "nested": {
-                "model_path": "/models/my_model",
-                "root_dir": "/home/user/projects",
-                "stats": {"steps": 1000, "epochs": 3},  # Should not be redacted
-            },
-        }
-
-        manager.send_event("test_event", test_properties)
-
-        # Verify the call was made
-        assert mock_capture.called
-
-        # Get the sanitized properties that were sent
-        sanitized = mock_capture.call_args[1]["properties"]
-
-        # Check that path-like and base_model keys were redacted
-        assert sanitized["filepath"] == "[REDACTED]"
-        assert sanitized["windows_path"] == "[REDACTED]"
-        assert sanitized["path_to_model"] == "[REDACTED]"
-        assert sanitized["base_model"] == "[REDACTED]"
-
-        # Check that non-path values were preserved
-        assert sanitized["message"] == "Training started"
-        assert sanitized["metrics"] == {"loss": 0.5, "accuracy": 0.95}
-
-        # Check nested structure handling
-        assert sanitized["nested"]["model_path"] == "[REDACTED]"
-        assert sanitized["nested"]["root_dir"] == "[REDACTED]"
-        assert sanitized["nested"]["stats"] == {"steps": 1000, "epochs": 3}
-
-
-def test_disable_telemetry(manager):
-    """Test that disabled telemetry doesn't send events"""
-    with patch("posthog.capture") as mock_capture:
-        manager.enabled = False
-        manager.send_event("test_event")
-        assert not mock_capture.called
-
-
-def test_exception_handling_during_send(manager):
-    """Test that exceptions in PostHog are handled gracefully"""
-    with patch("posthog.capture", side_effect=Exception("Test error")), patch(
-        "logging.Logger.warning"
-    ) as mock_warning:
-        manager.send_event("test_event")
-        warning_logged = False
-        for call in mock_warning.call_args_list:
-            if "Failed to send telemetry event" in str(call):
-                warning_logged = True
-                break
-        assert warning_logged
-
-
-def test_shutdown(manager):
-    """Test shutdown behavior"""
-    with patch("posthog.flush") as mock_flush:
-        manager.shutdown()
-        assert mock_flush.called

tests/telemetry/test_runtime_metrics.py

@@ -1,356 +0,0 @@
-"""Tests for runtime metrics telemetry module"""
-# pylint: disable=redefined-outer-name
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from axolotl.telemetry.runtime_metrics import RuntimeMetrics, RuntimeMetricsTracker
-
-
-@pytest.fixture
-def mock_time():
-    """Mock time.time() to have predictable values in tests"""
-    with patch("time.time") as mock_time:
-        # Start with time 1000.0 and increment by 10 seconds on each call
-        times = [1000.0 + i * 10 for i in range(10)]
-        mock_time.side_effect = times
-        yield mock_time
-
-
-@pytest.fixture
-def mock_telemetry_manager():
-    """Create a mock TelemetryManager"""
-    with patch(
-        "axolotl.telemetry.runtime_metrics.TelemetryManager"
-    ) as mock_manager_class:
-        mock_manager = MagicMock()
-        mock_manager.enabled = True
-        mock_manager_class.get_instance.return_value = mock_manager
-        yield mock_manager
-
-
-@pytest.fixture
-def mock_psutil():
-    """Mock psutil for memory information"""
-    with patch("axolotl.telemetry.runtime_metrics.psutil") as mock_psutil:
-        mock_process = MagicMock()
-        mock_memory_info = MagicMock()
-        # Set initial memory to 1GB
-        mock_memory_info.rss = 1024 * 1024 * 1024
-        mock_process.memory_info.return_value = mock_memory_info
-        mock_psutil.Process.return_value = mock_process
-        yield mock_psutil
-
-
-@pytest.fixture
-def mock_torch():
-    """Mock torch.cuda functions"""
-    with patch("axolotl.telemetry.runtime_metrics.torch") as mock_torch:
-        mock_torch.cuda.is_available.return_value = True
-        mock_torch.cuda.device_count.return_value = 2
-
-        # Mock memory allocated per device (1GB for device 0, 2GB for device 1)
-        mock_torch.cuda.memory_allocated.side_effect = (
-            lambda device: (device + 1) * 1024 * 1024 * 1024
-        )
-
-        yield mock_torch
-
-
-class TestRuntimeMetrics:
-    """Tests for RuntimeMetrics class."""
-
-    def test_initialization(self):
-        """Test RuntimeMetrics initialization."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-
-        assert metrics.start_time == 1000.0
-        assert metrics.epoch_start_times == {}
-        assert metrics.epoch_end_times == {}
-        assert metrics.peak_gpu_memory == {}
-        assert metrics.total_steps == 0
-        assert metrics.current_epoch == 0
-        assert metrics.current_step == 0
-        assert metrics.peak_cpu_memory == 0
-
-    def test_elapsed_time(self, mock_time):
-        """Test elapsed_time property."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-
-        # Mock time.time() to return 1050.0
-        mock_time.side_effect = [1050.0]
-
-        assert metrics.elapsed_time == 50.0
-
-    def test_epoch_time(self):
-        """Test epoch_time method."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-
-        # No epoch data
-        assert metrics.epoch_time(0) is None
-
-        # Add epoch start but no end
-        metrics.epoch_start_times[0] = 1000.0
-        assert metrics.epoch_time(0) is None
-
-        # Add epoch end
-        metrics.epoch_end_times[0] = 1060.0
-        assert metrics.epoch_time(0) == 60.0
-
-    def test_average_epoch_time(self):
-        """Test average_epoch_time method."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-
-        # No completed epochs
-        assert metrics.average_epoch_time() is None
-
-        # Add one completed epoch
-        metrics.epoch_start_times[0] = 1000.0
-        metrics.epoch_end_times[0] = 1060.0
-        assert metrics.average_epoch_time() == 60.0
-
-        # Add second completed epoch
-        metrics.epoch_start_times[1] = 1060.0
-        metrics.epoch_end_times[1] = 1140.0  # 80 seconds
-        assert metrics.average_epoch_time() == 70.0  # Average of 60 and 80
-
-        # Add incomplete epoch (should not affect average)
-        metrics.epoch_start_times[2] = 1140.0
-        assert metrics.average_epoch_time() == 70.0
-
-    def test_steps_per_second(self, mock_time):
-        """Test steps_per_second method."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-
-        # No steps - first call to time.time()
-        mock_time.side_effect = None
-        mock_time.return_value = 1050.0
-        assert metrics.steps_per_second() is None
-
-        # Add steps - second call to time.time()
-        metrics.total_steps = 100
-        mock_time.return_value = 1050.0  # Keep same time for consistent result
-        assert metrics.steps_per_second() == 2.0  # 100 steps / 50 seconds
-
-    def test_to_dict_basic(self, mock_time):
-        """Test to_dict method with basic metrics."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-        metrics.total_steps = 100
-        metrics.peak_cpu_memory = 2 * 1024 * 1024 * 1024  # 2GB
-
-        # Mock elapsed_time
-        mock_time.side_effect = None
-        mock_time.return_value = 1050.0
-
-        result = metrics.to_dict()
-
-        assert result["total_time_seconds"] == 50.0
-        assert result["total_steps"] == 100
-        assert result["steps_per_second"] == 2.0
-        assert result["epochs_completed"] == 0
-        assert result["peak_cpu_memory_bytes"] == 2 * 1024 * 1024 * 1024
-        assert "epoch_times" not in result
-        assert "gpu_memory" not in result
-
-    def test_to_dict_with_epochs(self, mock_time):
-        """Test to_dict method with epoch data."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-        metrics.total_steps = 100
-
-        # Add epoch data
-        metrics.epoch_start_times[0] = 1000.0
-        metrics.epoch_end_times[0] = 1060.0
-        metrics.epoch_start_times[1] = 1060.0
-        metrics.epoch_end_times[1] = 1140.0
-
-        # Mock elapsed_time
-        mock_time.side_effect = None
-        mock_time.return_value = 1150.0
-
-        result = metrics.to_dict()
-
-        assert "epoch_times" in result
-        assert result["epoch_times"]["epoch_0_seconds"] == 60.0
-        assert result["epoch_times"]["epoch_1_seconds"] == 80.0
-        assert result["average_epoch_time_seconds"] == 70.0
-
-    def test_to_dict_with_gpu_memory(self, mock_time):
-        """Test to_dict method with GPU memory data."""
-        metrics = RuntimeMetrics(start_time=1000.0)
-        metrics.peak_gpu_memory = {
-            0: 1 * 1024 * 1024 * 1024,  # 1GB
-            1: 2 * 1024 * 1024 * 1024,  # 2GB
-        }
-
-        # Mock elapsed_time
-        mock_time.side_effect = [1050.0]
-
-        result = metrics.to_dict()
-
-        assert "gpu_memory" in result
-        assert result["gpu_memory"]["gpu_0_peak_memory_bytes"] == 1 * 1024 * 1024 * 1024
-        assert result["gpu_memory"]["gpu_1_peak_memory_bytes"] == 2 * 1024 * 1024 * 1024
-
-
-class TestRuntimeMetricsTracker:
-    """Tests for RuntimeMetricsTracker class."""
-
-    # pylint: disable=unused-argument
-    def test_initialization(self, mock_time, mock_telemetry_manager):
-        """Test RuntimeMetricsTracker initialization."""
-        tracker = RuntimeMetricsTracker()
-
-        assert isinstance(tracker.metrics, RuntimeMetrics)
-        assert tracker.metrics.start_time == 1000.0  # First value from mock_time
-
-    # pylint: disable=unused-argument
-    def test_start_epoch(
-        self, mock_time, mock_psutil, mock_torch, mock_telemetry_manager
-    ):
-        """Test start_epoch method."""
-        tracker = RuntimeMetricsTracker()
-
-        # Reset mock_time to control next value
-        mock_time.side_effect = [1010.0]
-
-        tracker.start_epoch(0)
-
-        assert tracker.metrics.current_epoch == 0
-        assert tracker.metrics.epoch_start_times[0] == 1010.0
-
-        # Verify memory metrics were updated
-        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
-        assert 0 in tracker.metrics.peak_gpu_memory
-        assert 1 in tracker.metrics.peak_gpu_memory
-
-    # pylint: disable=unused-argument
-    def test_end_epoch(self, mock_time, mock_telemetry_manager):
-        """Test end_epoch method."""
-        tracker = RuntimeMetricsTracker()
-
-        # Start epoch 0
-        mock_time.side_effect = [1010.0]
-        tracker.start_epoch(0)
-
-        # End epoch 0
-        mock_time.side_effect = [1060.0]
-        tracker.end_epoch(0)
-
-        assert 0 in tracker.metrics.epoch_end_times
-        assert tracker.metrics.epoch_end_times[0] == 1060.0
-
-    # pylint: disable=unused-argument
-    def test_update_step(
-        self, mock_time, mock_psutil, mock_torch, mock_telemetry_manager
-    ):
-        """Test update_step method."""
-        tracker = RuntimeMetricsTracker()
-
-        # Update step to a non-multiple of 100
-        tracker.update_step(42)
-
-        assert tracker.metrics.current_step == 42
-        assert tracker.metrics.total_steps == 1
-
-        # Memory metrics should not be updated for non-multiple of 100
-        assert tracker.metrics.peak_cpu_memory == 0
-
-        # Update step to a multiple of 100
-        tracker.update_step(100)
-
-        assert tracker.metrics.current_step == 100
-        assert tracker.metrics.total_steps == 2
-
-        # Memory metrics should be updated for multiple of 100
-        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
-
-    # pylint: disable=unused-argument
-    def test_update_memory_metrics(
-        self, mock_psutil, mock_torch, mock_telemetry_manager
-    ):
-        """Test update_memory_metrics method."""
-        tracker = RuntimeMetricsTracker()
-
-        # Initial memory state
-        assert tracker.metrics.peak_cpu_memory == 0
-        assert tracker.metrics.peak_gpu_memory == {}
-
-        # Update memory metrics
-        tracker.update_memory_metrics()
-
-        # Verify CPU memory
-        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
-
-        # Verify GPU memory
-        assert tracker.metrics.peak_gpu_memory[0] == 1 * 1024 * 1024 * 1024
-        assert tracker.metrics.peak_gpu_memory[1] == 2 * 1024 * 1024 * 1024
-
-        # Change mocked memory values to be lower
-        mock_process = mock_psutil.Process.return_value
-        mock_memory_info = mock_process.memory_info.return_value
-        mock_memory_info.rss = 0.5 * 1024 * 1024 * 1024  # 0.5GB
-
-        mock_torch.cuda.memory_allocated.side_effect = (
-            lambda device: (device + 0.5) * 1024 * 1024 * 1024
-        )
-
-        # Update memory metrics again
-        tracker.update_memory_metrics()
-
-        # Peak values should not decrease
-        assert tracker.metrics.peak_cpu_memory == 1 * 1024 * 1024 * 1024
-        assert tracker.metrics.peak_gpu_memory[0] == 1 * 1024 * 1024 * 1024
-        assert tracker.metrics.peak_gpu_memory[1] == 2 * 1024 * 1024 * 1024
-
-        # Change mocked memory values to be higher
-        mock_memory_info.rss = 2 * 1024 * 1024 * 1024  # 2GB
-
-        mock_torch.cuda.memory_allocated.side_effect = (
-            lambda device: (device + 2) * 1024 * 1024 * 1024
-        )
-
-        # Update memory metrics again
-        tracker.update_memory_metrics()
-
-        # Peak values should increase
-        assert tracker.metrics.peak_cpu_memory == 2 * 1024 * 1024 * 1024
-        assert tracker.metrics.peak_gpu_memory[0] == 2 * 1024 * 1024 * 1024
-        assert tracker.metrics.peak_gpu_memory[1] == 3 * 1024 * 1024 * 1024
-
-    # pylint: disable=unused-argument
-    def test_get_memory_metrics(self, mock_psutil, mock_torch, mock_telemetry_manager):
-        """Test get_memory_metrics method."""
-        tracker = RuntimeMetricsTracker()
-
-        # Set peak memory values
-        tracker.metrics.peak_cpu_memory = 2 * 1024 * 1024 * 1024
-        tracker.metrics.peak_gpu_memory = {
-            0: 3 * 1024 * 1024 * 1024,
-            1: 4 * 1024 * 1024 * 1024,
-        }
-
-        # Get memory metrics
-        memory_metrics = tracker.get_memory_metrics()
-
-        # Verify CPU memory
-        assert (
-            memory_metrics["cpu_memory_bytes"] == 1 * 1024 * 1024 * 1024
-        )  # Current value from mock
-        assert (
-            memory_metrics["peak_cpu_memory_bytes"] == 2 * 1024 * 1024 * 1024
-        )  # Peak value we set
-
-        # Verify GPU memory
-        assert (
-            memory_metrics["gpu_0_memory_bytes"] == 1 * 1024 * 1024 * 1024
-        )  # Current value from mock
-        assert (
-            memory_metrics["gpu_0_peak_memory_bytes"] == 3 * 1024 * 1024 * 1024
-        )  # Peak value we set
-        assert (
-            memory_metrics["gpu_1_memory_bytes"] == 2 * 1024 * 1024 * 1024
-        )  # Current value from mock
-        assert (
-            memory_metrics["gpu_1_peak_memory_bytes"] == 4 * 1024 * 1024 * 1024
-        )  # Peak value we set