fix: Handle disabled validation in SFT training

[sahgerlad]

What does this PR do ?

Handle disabled validation in SFT training

Summary by CodeRabbit

• Bug Fixes
  • Improved robustness when validation data is not provided
  • Fixed potential errors in token counting and validation timing metric calculations
  • Enhanced validation configuration error messages

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[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request improves robustness in the SFT algorithm implementation by conditionally instantiating the validation dataloader only when a validation dataset exists, adjusting validation flow assertions, and using safer dictionary access patterns with defaults to prevent KeyErrors when metrics are unavailable.

Changes

Cohort / File(s)	Summary
Validation dataloader and metrics robustness nemo_rl/algorithms/sft.py	Conditional creation of val_dataloader (only if val_dataset is not None); updated validation assertion to reference sft.val_period instead of dpo.val_period; replaced direct dictionary indexing with .get() calls for global_valid_toks metric access to prevent KeyErrors in token counting and timing computation

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Defensive programming improvements: All .get() substitutions follow a consistent pattern and are low-risk
• Conditional dataloader logic: Straightforward null-check guard with no complex downstream dependencies
• Assertion correction: Verify that the assertion change (from dpo.val_period to sft.val_period) aligns with the correct module context and intended validation behavior

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title 'fix: Handle disabled validation in SFT training' directly and clearly describes the main change: handling disabled validation in SFT training, which aligns with the PR objectives and file modifications.
Test Results For Major Changes	✅ Passed	PR contains only minor changes: bug fixes (incorrect variable reference) and defensive programming (None checks, safe dictionary access). No new features, breaking changes, or performance/convergence impacts.

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Actionable comments posted: 0

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⚠️ Outside diff range comments (1)

nemo_rl/algorithms/sft.py (1)

234-252: Ensure validate() always returns a 2‑tuple when called, even when val_dataloader is None

With the new early return:

if val_dataloader is None:
    assert master_config["sft"]["val_period"] <= 0, (
        "val_dataloader is None, so sft.val_period must be <= 0"
    )
    print("  ⚠️ No validation dataloader provided, skipping validation")
    return

validate() returns None when val_dataloader is None and sft.val_period <= 0. In sft_train() you always unpack:

val_metrics, validation_timings = validate(...)

This will raise TypeError: cannot unpack non-iterable NoneType object in configurations like:

• val_dataset is None
• sft.val_period <= 0 (validation disabled)
• sft.val_at_start is True (so the initial validation call runs)

To make this path safe while preserving the assertion for misconfigurations, have validate() always return a 2‑tuple when it returns normally, e.g.:

-    if val_dataloader is None:
-        assert master_config["sft"]["val_period"] <= 0, (
-            "val_dataloader is None, so sft.val_period must be <= 0"
-        )
-        print("  ⚠️ No validation dataloader provided, skipping validation")
-        return
+    if val_dataloader is None:
+        assert master_config["sft"]["val_period"] <= 0, (
+            "val_dataloader is None, so sft.val_period must be <= 0"
+        )
+        print("  ⚠️ No validation dataloader provided, skipping validation")
+        # Return empty metric dicts so callers that unpack and log still work.
+        return {}, {}

Callers then safely unpack and log, and you still get a hard failure if val_period > 0 with no validation dataloader.

🧹 Nitpick comments (2)

nemo_rl/algorithms/sft.py (2)

146-162: Conditional val_dataloader creation is good; update typing to reflect Optional usage

Making val_dataloader conditional on val_dataset is not None correctly avoids constructing a dataloader when validation data is absent. However, the type hints now no longer match behavior:

• setup() still annotates val_dataset: AllTaskProcessedDataset and the 4th return element as StatefulDataLoader, but it can now be None.
• validate() still takes val_dataloader: StatefulDataLoader, though it explicitly handles None.

To keep static typing accurate, consider:

-def setup(
-    master_config: MasterConfig,
-    tokenizer: AutoTokenizer,
-    train_dataset: AllTaskProcessedDataset,
-    val_dataset: AllTaskProcessedDataset,
-) -> tuple[
-    Policy,
-    RayVirtualCluster,
-    StatefulDataLoader,
-    StatefulDataLoader,
+def setup(
+    master_config: MasterConfig,
+    tokenizer: AutoTokenizer,
+    train_dataset: AllTaskProcessedDataset,
+    val_dataset: Optional[AllTaskProcessedDataset],
+) -> tuple[
+    Policy,
+    RayVirtualCluster,
+    StatefulDataLoader,
+    Optional[StatefulDataLoader],

and in validate:

-def validate(
-    policy: PolicyInterface,
-    val_dataloader: StatefulDataLoader,
+def validate(
+    policy: PolicyInterface,
+    val_dataloader: Optional[StatefulDataLoader],

This keeps annotations aligned with the new “validation may be disabled” behavior.

---

612-618: Guard valid_tokens_per_sec_per_gpu against zero total_time

Using .get here avoids a KeyError when global_valid_toks is missing:

timing_metrics["valid_tokens_per_sec_per_gpu"] = (
    metrics.get("global_valid_toks", 0) / total_time / total_num_gpus
)

However, if total_time is 0 (e.g., very fast steps or missing timer entries), this will still raise ZeroDivisionError. You already guard against this when printing percentages above, so you can mirror that pattern:

-            timing_metrics["valid_tokens_per_sec_per_gpu"] = (
-                metrics.get("global_valid_toks", 0) / total_time / total_num_gpus
-            )
+            if total_time > 0:
+                timing_metrics["valid_tokens_per_sec_per_gpu"] = (
+                    metrics.get("global_valid_toks", 0) / total_time / total_num_gpus
+                )
+            else:
+                timing_metrics["valid_tokens_per_sec_per_gpu"] = 0.0

This keeps logging robust even in edge timing cases.

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• nemo_rl/algorithms/sft.py (4 hunks)

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nemo_rl/algorithms/sft.py (1)

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🔇 Additional comments (1)

nemo_rl/algorithms/sft.py (1)

488-503: Using metrics.get("global_valid_toks", 0) for accumulation is a solid robustness improvement

Switching from direct indexing to:

total_valid_tokens += metrics.get("global_valid_toks", 0)

prevents a KeyError when global_valid_toks is absent from train_results["all_mb_metrics"], while preserving the previous behavior when it is present. This also keeps backward compatibility with older checkpoints via the default initialization of total_valid_tokens.

No further changes needed here.

[terrykong]

@ashors1 to review

[ashors1]

Looks good to me! Thanks for the PR!