feat: add class-based callback system for training lifecycle hooks

src/instructlab/training/__init__.py

@@ -5,7 +5,9 @@
     "LoraOptions",
     "QuantizeDataType",
     "TorchrunArgs",
+    "TrainerCallback",
     "TrainingArgs",
+    "TrainingContext",
     "run_training",
     "FSDPOptions",
     "ShardingStrategies",
@@ -17,6 +19,7 @@
 import instructlab.training.logger  # Disable package logging by default
 
 # Local
+from .callbacks import TrainerCallback, TrainingContext
 from .config import (
     DataProcessArgs,
     DeepSpeedOffloadStrategy,

src/instructlab/training/batch_loss_manager.py

@@ -48,7 +48,14 @@ class BatchLossManager:
     - Computing average losses for logging
     """
 
-    def __init__(self, model, accelerator, world_size: int, local_rank: int):
+    def __init__(
+        self,
+        model,
+        accelerator,
+        world_size: int,
+        local_rank: int,
+        callback_manager=None,
+    ):
         """
         Initialize the BatchLossManager.
 
@@ -57,12 +64,14 @@ def __init__(self, model, accelerator, world_size: int, local_rank: int):
             accelerator: The accelerator instance for distributed training
             world_size: Number of distributed processes
             local_rank: Local rank of the current process
+            callback_manager: Optional CallbackManager for lifecycle hooks
         """
         self.model: Model = model
         self.accelerator: Accelerator = accelerator
         self.world_size: int = world_size
         self.local_rank: int = local_rank
         self.torch_device = torch.device("cuda", local_rank)
+        self.callback_manager = callback_manager
 
     def process_batch(
         self,
@@ -111,6 +120,9 @@ def process_batch(
             batch_total_samples += micro_batch_size
             batch_total_length += total_length
 
+            if self.callback_manager:
+                self.callback_manager.fire("on_before_forward")
+
             # prepare model inputs
             model_inputs = self._prepare_model_inputs(mb)
 
@@ -126,6 +138,9 @@ def process_batch(
 
             self.accelerator.backward(scaled_loss)
 
+            if self.callback_manager:
+                self.callback_manager.fire("on_after_backward")
+
             # accumulate losses
             grad_accum_steps += 1
             accumulated_loss += raw_losses.main_loss

src/instructlab/training/callbacks.py

@@ -0,0 +1,280 @@
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Callback system for training lifecycle hooks.
+
+Provides async, fire-and-forget callbacks that observe training events
+without blocking the training loop or propagating exceptions.
+"""
+
+# Standard
+from dataclasses import dataclass, field
+from typing import Any
+import asyncio
+import base64
+import copy
+import inspect
+import json
+import logging
+import textwrap
+import threading
+
+logger = logging.getLogger("instructlab.training")
+
+HOOK_NAMES = [
+    "on_train_begin",
+    "on_epoch_begin",
+    "on_step_begin",
+    "on_before_forward",
+    "on_after_backward",
+    "on_pre_optimizer_step",
+    "on_optimizer_step",
+    "on_log",
+    "on_evaluate",
+    "on_save",
+    "on_step_end",
+    "on_epoch_end",
+    "on_train_end",
+]
+
+
+@dataclass
+class TrainingContext:
+    """Mutable training state maintained by the training loop.
+
+    The CallbackManager snapshots this before dispatching to callbacks,
+    so callback authors receive an effectively read-only view.
+    """
+
+    hook_name: str = ""
+
+    step: int = 0
+    epoch: int = 0
+    total_samples: int = 0
+    total_tokens: int = 0
+
+    loss: float | None = None
+    learning_rate: float | None = None
+    grad_norm: float | None = None
+    elapsed_time: float | None = None
+    overall_throughput: float | None = None
+    cuda_mem_allocated: float | None = None
+
+    batch_metrics: dict[str, Any] = field(default_factory=dict)
+    val_metrics: dict[str, Any] = field(default_factory=dict)
+    checkpoint_path: str | None = None
+
+    output_dir: str = ""
+    model_name_or_path: str = ""
+    max_epochs: int = 0
+    world_size: int = 1
+    is_local_process_zero: bool = True
+    is_world_process_zero: bool = True
+
+
+class TrainerCallback:
+    """Base class for training callbacks. Subclass and override hooks you need.
+
+    Callbacks fire on ALL distributed ranks. Use context.is_world_process_zero
+    or context.is_local_process_zero to gate rank-specific side effects
+    (logging, saving, external API calls).
+    """
+
+    def on_train_begin(self, context: TrainingContext) -> None:
+        pass
+
+    def on_epoch_begin(self, context: TrainingContext) -> None:
+        pass
+
+    def on_step_begin(self, context: TrainingContext) -> None:
+        pass
+
+    def on_before_forward(self, context: TrainingContext) -> None:
+        pass
+
+    def on_after_backward(self, context: TrainingContext) -> None:
+        pass
+
+    def on_pre_optimizer_step(self, context: TrainingContext) -> None:
+        pass
+
+    def on_optimizer_step(self, context: TrainingContext) -> None:
+        pass
+
+    def on_log(self, context: TrainingContext) -> None:
+        pass
+
+    def on_evaluate(self, context: TrainingContext) -> None:
+        pass
+
+    def on_save(self, context: TrainingContext) -> None:
+        pass
+
+    def on_step_end(self, context: TrainingContext) -> None:
+        pass
+
+    def on_epoch_end(self, context: TrainingContext) -> None:
+        pass
+
+    def on_train_end(self, context: TrainingContext) -> None:
+        pass
+
+
+class CallbackManager:
+    """Dispatches lifecycle hooks to registered TrainerCallback instances."""
+
+    def __init__(self):
+        self._callbacks: list[TrainerCallback] = []
+        self.context = TrainingContext()
+
+        self._loop = asyncio.new_event_loop()
+        self._thread = threading.Thread(target=self._run_event_loop, daemon=True)
+        self._thread.start()
+
+    def _run_event_loop(self):
+        asyncio.set_event_loop(self._loop)
+        self._loop.run_forever()
+
+    def add_callback(self, callback: TrainerCallback) -> None:
+        if not isinstance(callback, TrainerCallback):
+            raise TypeError(
+                f"Expected a TrainerCallback instance, got "
+                f"{type(callback).__name__}. "
+                f"Pass an instance, not a class: callbacks=[MyCallback()]"
+            )
+        self._callbacks.append(callback)
+
+    def remove_callback(self, callback_or_type) -> None:
+        if isinstance(callback_or_type, type):
+            self._callbacks = [
+                cb for cb in self._callbacks if not isinstance(cb, callback_or_type)
+            ]
+        else:
+            self._callbacks = [
+                cb for cb in self._callbacks if cb is not callback_or_type
+            ]
+
+    def fire(self, hook_name: str, **kwargs) -> None:
+        if not self.has_callbacks(hook_name):
+            return
+
+        snapshot = copy.copy(self.context)
+        snapshot.hook_name = hook_name
+        snapshot.batch_metrics = dict(snapshot.batch_metrics)
+        snapshot.val_metrics = dict(snapshot.val_metrics)
+        _valid_fields = {f.name for f in snapshot.__dataclass_fields__.values()}
+        for key, value in kwargs.items():
+            if key not in _valid_fields:
+                raise ValueError(
+                    f"Unknown TrainingContext field: '{key}'. Valid fields: {sorted(_valid_fields)}"
+                )
+            setattr(snapshot, key, value)
+
+        for callback in self._callbacks:
+            method = getattr(callback, hook_name)
+            if getattr(type(callback), hook_name) is getattr(
+                TrainerCallback, hook_name
+            ):
+                continue
+            future = asyncio.run_coroutine_threadsafe(
+                self._safe_invoke(method, snapshot), self._loop
+            )
+            if hook_name == "on_train_end":
+                try:
+                    future.result(timeout=10)
+                except TimeoutError:
+                    logger.warning(
+                        "Callback %s.%s timed out during on_train_end (10s limit).",
+                        type(callback).__name__,
+                        hook_name,
+                    )
+                except Exception:
+                    pass
+
+    async def _safe_invoke(self, method, context: TrainingContext) -> None:
+        try:
+            result = method(context)
+            if asyncio.iscoroutine(result):
+                await result
+        except Exception:
+            logger.exception(
+                "Callback %s.%s raised an exception (hook=%s, step=%d). "
+                "This exception is suppressed and will not affect training.",
+                type(method.__self__).__name__
+                if hasattr(method, "__self__")
+                else repr(method),
+                method.__name__,
+                context.hook_name,
+                context.step,
+            )
+
+    def has_callbacks(self, hook_name: str) -> bool:
+        base_method = getattr(TrainerCallback, hook_name)
+        return any(
+            getattr(type(cb), hook_name) is not base_method for cb in self._callbacks
+        )
+
+    def close(self) -> None:
+        """Shut down the background event loop and thread."""
+        self._loop.call_soon_threadsafe(self._loop.stop)
+        self._thread.join(timeout=5)
+        self._loop.close()
+
+
+def serialize_callback(callback: TrainerCallback) -> str:
+    """Serialize a TrainerCallback subclass to a base64 string.
+
+    Callbacks must be self-contained classes with zero-argument constructors.
+    Any imports needed inside hooks should be inline (inside the method body),
+    not at module level.
+    """
+    cls = type(callback)
+    try:
+        cls()
+    except TypeError as e:
+        raise TypeError(
+            f"Callback {cls.__name__} must have a zero-argument constructor "
+            f"to be serializable across the torchrun subprocess boundary: {e}"
+        ) from e
+    source = inspect.getsource(cls)
+    source = textwrap.dedent(source)
+    return base64.b64encode(source.encode("utf-8")).decode("ascii")
+
+
+def deserialize_callback(encoded: str) -> TrainerCallback:
+    """Reconstruct a TrainerCallback instance from a base64-encoded class source."""
+    source = base64.b64decode(encoded).decode("utf-8")
+    namespace: dict[str, Any] = {
+        "TrainerCallback": TrainerCallback,
+        "TrainingContext": TrainingContext,
+    }
+    exec(source, namespace)  # noqa: S102
+    classes = [
+        v
+        for v in namespace.values()
+        if isinstance(v, type)
+        and issubclass(v, TrainerCallback)
+        and v is not TrainerCallback
+    ]
+    if len(classes) != 1:
+        raise ValueError(
+            f"Expected exactly one TrainerCallback subclass, "
+            f"got {len(classes)}. Source:\n{source}"
+        )
+    return classes[0]()
+
+
+def serialize_callbacks_for_cli(
+    callbacks: list[TrainerCallback],
+) -> str:
+    """Serialize a list of callbacks to a base64 string for CLI transport."""
+    serialized = [serialize_callback(cb) for cb in callbacks]
+    return base64.b64encode(json.dumps(serialized).encode("utf-8")).decode("ascii")
+
+
+def deserialize_callbacks_from_cli(
+    encoded: str,
+) -> list[TrainerCallback]:
+    """Reconstruct TrainerCallback instances from a CLI-transported base64 string."""
+    decoded = json.loads(base64.b64decode(encoded).decode("utf-8"))
+    return [deserialize_callback(s) for s in decoded]

src/instructlab/training/config.py

@@ -191,7 +191,7 @@ class TrainingArgs(BaseModel):
     """
 
     # disable the protected namespace for the model_config field
-    model_config = ConfigDict(protected_namespaces=())
+    model_config = ConfigDict(protected_namespaces=(), arbitrary_types_allowed=True)
 
     # Either the name of a HuggingFace model or a path to a model saved in HuggingFace format.
     model_path: str
@@ -374,6 +374,12 @@ class TrainingArgs(BaseModel):
         ),
     )
 
+    callbacks: list | None = Field(
+        default=None,
+        exclude=True,
+        description="List of TrainerCallback instances for training lifecycle hooks.",
+    )
+
     @model_validator(mode="after")
     def validate_validation_config(self):
         if not 0.0 <= self.validation_split < 1.0: