minor

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>

Author: kevalmorabia97

examples/megatron_bridge/prune_minitron.py

@@ -140,11 +140,11 @@ def get_args() -> argparse.Namespace:
     parser.add_argument(
         "--prune_score_func",
         type=str,
-        default="mmlu_10pct",
+        default="mmlu_5pct",
         help=(
             "Score function to use for NAS-based pruning (--prune_target_params). Only supports MMLU at the moment. "
             "Format: mmlu_<N>pct where <N> is the percentage of MMLU data to sample per subject "
-            "(e.g. mmlu_10pct for 10%, mmlu_100pct for full eval)."
+            "(e.g. mmlu_5pct for 5%, mmlu_100pct for full eval)."
         ),
     )
     parser.add_argument(
@@ -299,17 +299,13 @@ def main(args: argparse.Namespace):
         match = re.fullmatch(r"mmlu_(\d+)pct", args.prune_score_func)
         if not match:
             raise ValueError(
-                f"Invalid score function: {args.prune_score_func}. "
-                "Expected format: mmlu_<N>pct (e.g. mmlu_10pct)"
+                f"Invalid score function: {args.prune_score_func}. Expected format: mmlu_<N>pct (e.g. mmlu_5pct)"
             )
-        mmlu_pct = int(match.group(1))
-        if not 0 < mmlu_pct <= 100:
-            raise ValueError("--prune_score_func percentage must be in the range [1, 100].")
-        _mmlu_frac = mmlu_pct / 100.0
+        mmlu_frac = float(match.group(1)) / 100.0
 
         def score_func(m):
             return megatron_mmlu(
-                m, tokenizer, few_shots=0, fraction=_mmlu_frac, batch_size=args.calib_mbs
+                m, tokenizer, few_shots=0, fraction=mmlu_frac, batch_size=args.calib_mbs
             )
 
         pruning_config["score_func"] = score_func

examples/pruning/README.md

@@ -124,7 +124,7 @@ This mode can be useful when you don't know the exact dimensions you want to pru
 from modelopt.torch.utils.plugins.megatron_mmlu import megatron_mmlu
 
 def score_func(m):
-    return megatron_mmlu(m, tokenizer, fraction=0.1, batch_size=4)  # 10% sampled data for faster eval
+    return megatron_mmlu(m, tokenizer, fraction=0.05, batch_size=4)  # 5% sampled data for faster eval
 
 # Specify target parameter count and configure the auto pruning algorithm
 # Save minitron scores at checkpoint so we can resume pruning without running the forward loop again
@@ -147,7 +147,7 @@ mtp.prune(...)
 
 1. **Importance Scoring**: Same as manual pruning - computes activation magnitudes for all parameters (takes ~5 minutes for an 8B model)
 2. **Search Space Construction**: Generates a search space of possible architectures based search space config and other configs (`max_width_pruning`, `max_depth_pruning`, `hparams_to_skip`)
-3. **Architecture Search**: Find candidate architectures that meet the parameter constraint and evaluate `top_k` (based on number of parameters) of them using `score_func` e.g. MMLU, negative validation loss, etc. (takes ~5 min per candidate for an 8B model MMLU score with 10% sampled data)
+3. **Architecture Search**: Find candidate architectures that meet the parameter constraint and evaluate `top_k` (based on number of parameters) of them using `score_func` e.g. MMLU, negative validation loss, etc. (takes 2-3 mins per candidate for an 8B model MMLU score with 5% sampled data)
 4. **Best Architecture Selection**: Returns the architecture (best `export_config`) with the highest actual score from the top-K evaluated architectures
 5. **Weight Slicing**: Slices the model weights according to the best pruned architecture found
 

modelopt/torch/utils/plugins/megatron_mmlu.py

@@ -78,6 +78,7 @@ def megatron_mmlu(
         f"\nMMLU ({fraction * 100}%, {few_shots}-shot, Batch Size: {batch_size}) evaluation started...\n"
         "First batch may take longer to evaluate for Pipeline Parallel models."
     )
+    assert 0 < fraction <= 1, "Fraction must be between 0 and 1"
 
     # Token IDs for " A", " B", " C", " D" — the last subword handles edge cases.
     choice_ids = [tokenizer.encode(f" {c}", add_special_tokens=False)[-1] for c in _CHOICES]