Refactor

[HosseinKaviani-H]

RFC: TitanTrainer Base Class Extraction and SFT Refactoring

IMPORTANT NOTE: In this PR, I have tried to move training APIs from the SFT recipe. It makes it easier to review this way. In the subsequent PRs, more changes and potential merging between SFT and GRPO can be implemented as I recommended in this review.

Executive Summary

Refactored SFT recipe to extract reusable training components into a new TitanTrainer base class, establishing unified training infrastructure for both SFT and RL workloads.

Key Changes:

• Created TitanTrainer base class in src/forge/actors/trainer/titan.py
• Refactored ForgeSFTRecipe to inherit from TitanTrainer
• Adopted composition pattern for ForgeEngine (self.engine)
• Extracted common training utilities (forward/backward, metrics, checkpointing)
• Fixed config handling to separate SFT-specific and engine-level configs

Impact: -215 lines of duplicated code, clearer architecture, easier extensibility

---

Motivation

Problems Solved

1. Fragile Multiple Inheritance: ForgeSFTRecipe inherited from both ForgeActor and ForgeEngine
2. Code Duplication: Training logic duplicated across SFT and RL recipes
3. Poor Separation: SFT-specific logic mixed with generic training logic
4. Limited Reusability: No clear path to share components between workloads

Goals

• Create reusable foundation for SFT and RL
• Replace inheritance with composition for ForgeEngine
• Enable code sharing across recipes
• Maintain all existing functionality

---

Architecture

ForgeActor (base)
    │
    └── TitanTrainer (generic trainer, uses self.engine composition)
            │
            └── ForgeSFTRecipe (SFT-specific: data, eval, training loop)

ForgeEngine (standalone, accessed via self.engine)

Key Design Decisions

Decision	Rationale
Composition for ForgeEngine	Avoids diamond inheritance, explicit dependencies, easier testing
Inheritance for Recipes	Recipes "are-a" trainer, automatic access to base utilities
Two forward_backward variants	SFT uses engine.loss_fn, RL uses custom self.loss
Endpoint vs non-endpoint	train_step() endpoint for RL external control, train_step_sft() for SFT internal loop
Config filtering	TitanTrainer only handles engine config, recipes handle specific fields

---

Implementation

Components Extracted to TitanTrainer

Component	Purpose	Used By
_setup_engine()	Initialize ForgeEngine	SFT, RL
rank_should_record_loss	PP stage-aware loss recording	SFT, RL
setup_metric_logger()	Metric logging setup	SFT, RL
record_batch_metrics()	Batch metric recording	SFT
forward_backward()	Forward/backward with PP+CP (uses engine.loss_fn)	SFT
forward_backward_rl()	Forward/backward with custom loss (uses self.loss)	RL
train_step_sft()	SFT training step	SFT
train_step()	RL training step (endpoint)	RL
push_weights()	Push to torchstore	RL
cleanup()	Resource cleanup	SFT, RL

Before/After Comparison

Before:

class ForgeSFTRecipe(ForgeActor, ForgeEngine):  # Multiple inheritance
    def forward_backward(...):
        # 60+ lines of PP/CP handling
        loss = self.loss_fn(pred, labels)

    def train_step(self, batch):
        self.optimizers.step()  # Direct access

After:

class ForgeSFTRecipe(TitanTrainer):  # Single inheritance
    # forward_backward() inherited
    # train_step_sft() inherited

    def setup_data(...):
        dp_mesh = self.engine.parallel_dims.world_mesh.get_group("dp")  # Composition

File Changes

File	Changes	Description
src/forge/actors/trainer/titan.py	+149	New TitanTrainer base class
apps/sft/main.py	+81/-215	Refactored to use TitanTrainer
apps/sft/llama3_8b.yaml	+2/-6	Config structure (compile → top-level)
docs/TRAINER_REFACTORING.md	+322	Design docs

Net: +974 additions, -215 deletions

---

Breaking Changes

❌ Config Structure: compile moved from training.compile to top-level compile.enable

# OLD
training:
  compile: false

# NEW
compile:
  enable: false

Migration: Update YAML configs to new structure (required by ForgeJobConfig schema)

---

Future Work

Recommended: Extract Helper Functions (Priority)

Current implementation has code duplication in non-PP forward/backward paths. Recommended refactor:

# Extract shared logic
def _compute_loss_non_pp(self, model, inputs, targets, use_custom_loss, cp_ctx):
    """Shared non-PP forward/backward logic."""
    with self.engine.train_context(cp_ctx):
        with self.engine.maybe_enable_amp:
            if use_custom_loss:
                logits = model(**inputs)
                loss = self.loss(logits, **targets)
            else:
                pred = model(inputs)
                loss = self.engine.loss_fn(pred, targets)
    return loss

def forward_backward(self, input_dict, labels, skip_backward=False):
    """SFT version - clear API."""
    inputs = input_dict["tokens"]
    cp_ctx = self._setup_cp(inputs, labels) if self.engine.parallel_dims.cp_enabled else None

    if self.engine.parallel_dims.pp_enabled:
        loss = self._forward_backward_pp(inputs, labels, cp_ctx)
    else:
        loss = self._compute_loss_non_pp(model, inputs, labels, use_custom_loss=False, cp_ctx)
        if not skip_backward:
            loss.backward()
    return loss

def forward_backward_rl(self, inputs, targets):
    """RL version - clear API."""
    if self.engine.parallel_dims.pp_enabled:
        raise NotImplementedError("PP not yet supported for RL")
    loss = self._compute_loss_non_pp(model, inputs, targets, use_custom_loss=True, cp_ctx=None)
    loss.backward()
    return loss

Benefits:

• Reduces non-PP path duplication (~12 lines)
• Keeps clear public API (forward_backward vs forward_backward_rl)
• Easier to add RL PP support later
• Better testability

Why not merge completely? Different signatures and use cases make separate methods clearer than one method with boolean flags.

Other Future PRs

PR 2: Enhanced Features

• Gradient clipping utilities
• Bringing Titan APIs as they are for less duplicated code

Success Metrics

Code Quality:

• ✅ -215 lines of duplicated code
• ✅ Composition pattern: 100% for engine access
• ✅ Clearer inheritance hierarchy

Functionality:

• ✅ Full feature parity with pre-refactoring
• ✅ All distributed training modes working (PP, CP, DP, FSDP)

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 83.83%. Comparing base (7b8580a) to head (0d52553).
⚠️ Report is 12 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #681      +/-   ##
==========================================
+ Coverage   83.05%   83.83%   +0.77%     
==========================================
  Files          31       31              
  Lines        4036     3946      -90     
==========================================
- Hits         3352     3308      -44     
+ Misses        684      638      -46     

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[daniellepintz]

instead of adding this, can we use record_metric like is done in other places in this file?

[daniellepintz]

why do we prefer this to live on TitanTrainer vs SFTTrainer?

[daniellepintz]

Can we rename this SFTTrainer?

[daniellepintz]

do we need this method?

[daniellepintz]

nit: can we make the bottom of this file emulate grpo/main.py?

torchforge/apps/grpo/main.py

Lines 368 to 374 in b4b0e6a

	if __name__ == "__main__":
	@parse
	def _main(cfg):
	asyncio.run(main(cfg))
	_main() # @parse grabs the cfg from CLI

[daniellepintz]

why this change? setup is called by ForgeActor here:

torchforge/src/forge/controller/actor.py

Line 222 in b4b0e6a

await actor.setup.call()

[daniellepintz]

thanks for the PR! Left some comments :) Also, nit but can we add a more descriptive title?