Add Prompt Tuning and Prefix Tuning baselines

- peft_baseline.py: Add PromptTuningConfig (L=5,10,20) and PrefixTuningConfig (L=5,10)
- run_all_methods.py: Add 5 new methods to dispatch (prompt_tuning_5/10/20, prefix_tuning_5/10)
  with per-method directory output structure

Prompt Tuning: params = L * H (e.g. 10*1536 = 15360 params = 30KB)
Prefix Tuning: params = L * num_layers * 2 * H (much larger)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 28 insertions, 2 deletions

diff --git a/baselines/peft_baseline.py b/baselines/peft_baseline.py
index 442ba60..246385f 100644
--- a/baselines/peft_baseline.py
+++ b/baselines/peft_baseline.py
@@ -1,4 +1,4 @@
-"""PEFT baselines: LoRA, Tiny LoRA, and VeRA.
+"""PEFT baselines: LoRA, Tiny LoRA, VeRA, Prompt Tuning, Prefix Tuning.
 
 Per-user adaptation on K support examples, then standard generation.
 Uses a class-based API to avoid repeated model wrapping/unwrapping.
@@ -11,7 +11,10 @@ Usage:
 """
 
 import torch
-from peft import LoraConfig, VeraConfig, get_peft_model, TaskType
+from peft import (
+    LoraConfig, VeraConfig, PromptTuningConfig, PrefixTuningConfig,
+    get_peft_model, TaskType, PromptTuningInit,
+)
 
 
 TARGET_MODULES = ["q_proj", "v_proj"]
@@ -43,6 +46,21 @@ def _make_vera_config(rank, target_modules=None):
     )
 
 
+def _make_prompt_tuning_config(num_virtual_tokens):
+    return PromptTuningConfig(
+        task_type=TaskType.CAUSAL_LM,
+        num_virtual_tokens=num_virtual_tokens,
+        prompt_tuning_init=PromptTuningInit.RANDOM,
+    )
+
+
+def _make_prefix_tuning_config(num_virtual_tokens):
+    return PrefixTuningConfig(
+        task_type=TaskType.CAUSAL_LM,
+        num_virtual_tokens=num_virtual_tokens,
+    )
+
+
 def get_lora_config(rank=8):
     return _make_lora_config(rank=rank)
 
@@ -55,6 +73,14 @@ def get_vera_config(rank=256):
     return _make_vera_config(rank=rank)
 
 
+def get_prompt_tuning_config(num_tokens=10):
+    return _make_prompt_tuning_config(num_virtual_tokens=num_tokens)
+
+
+def get_prefix_tuning_config(num_tokens=10):
+    return _make_prefix_tuning_config(num_virtual_tokens=num_tokens)
+
+
 class PEFTBaseline:
     """Manages a PEFT-wrapped model for repeated per-user adaptation."""