feat: add configurable residual processing to reduce peak VRAM usage (#239)

* refactor residual memory optimizations

* formatting

* Fixed config.py positioning and default

* fixed analyzier declaration in main.py

* removing del statements

* ruff

* small updates

* ty moveback ish

config.default.toml

@@ -137,6 +137,12 @@ refusal_markers = [
 # System prompt to use when prompting the model.
 system_prompt = "You are a helpful assistant."
 
+# Move intermediate analysis tensors (such as residuals and logprobs)
+# to CPU memory as soon as possible to reduce peak VRAM usage.
+# This lowers peak VRAM usage during residual analysis and evaluation,
+# but may slightly reduce performance due to host/device transfers.
+offload_outputs_to_cpu = true
+
 # Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
 [good_prompts]
 dataset = "mlabonne/harmless_alpaca"

src/heretic/config.py

@@ -397,6 +397,14 @@ class Settings(BaseSettings):
         description="System prompt to use when prompting the model.",
     )
 
+    offload_outputs_to_cpu: bool = Field(
+        default=True,
+        description=(
+            "Whether to move intermediate analysis tensors (such as residuals and logprobs) "
+            "to CPU memory as soon as possible to reduce peak VRAM usage."
+        ),
+    )
+
     good_prompts: DatasetSpecification = Field(
         default=DatasetSpecification(
             dataset="mlabonne/harmless_alpaca",

src/heretic/main.py

@@ -421,13 +421,33 @@ def run():
 
     print()
     print("Calculating per-layer refusal directions...")
-    print("* Obtaining residuals for good prompts...")
-    good_residuals = model.get_residuals_batched(good_prompts)
-    print("* Obtaining residuals for bad prompts...")
-    bad_residuals = model.get_residuals_batched(bad_prompts)
 
-    good_means = good_residuals.mean(dim=0)
-    bad_means = bad_residuals.mean(dim=0)
+    needs_full_residuals = settings.print_residual_geometry or settings.plot_residuals
+
+    good_residuals = None
+    bad_residuals = None
+
+    if needs_full_residuals:
+        print("* Obtaining residuals for good prompts...")
+        good_residuals = model.get_residuals_batched(good_prompts)
+        print("* Obtaining residuals for bad prompts...")
+        bad_residuals = model.get_residuals_batched(bad_prompts)
+
+        good_means = good_residuals.mean(dim=0)
+        bad_means = bad_residuals.mean(dim=0)
+
+        analyzer = Analyzer(settings, model, good_residuals, bad_residuals)
+
+        if settings.print_residual_geometry:
+            analyzer.print_residual_geometry()
+
+        if settings.plot_residuals:
+            analyzer.plot_residuals()
+    else:
+        print("* Obtaining residual mean for good prompts...")
+        good_means = model.get_residuals_mean(good_prompts)
+        print("* Obtaining residual mean for bad prompts...")
+        bad_means = model.get_residuals_mean(bad_prompts)
 
     refusal_directions = F.normalize(bad_means - good_means, p=2, dim=1)
 
@@ -442,14 +462,6 @@ def run():
         )
         refusal_directions = F.normalize(refusal_directions, p=2, dim=1)
 
-    analyzer = Analyzer(settings, model, good_residuals, bad_residuals)
-
-    if settings.print_residual_geometry:
-        analyzer.print_residual_geometry()
-
-    if settings.plot_residuals:
-        analyzer.plot_residuals()
-
     # We don't need the residuals after computing refusal directions.
     del good_residuals, bad_residuals, analyzer
     empty_cache()

src/heretic/model.py

@@ -636,6 +636,9 @@ class Model:
             max_new_tokens=1,
             output_hidden_states=True,
             return_dict_in_generate=True,
+            # KV cache is unnecessary here because we only need the hidden states
+            # for the first generated token.
+            use_cache=False,
         )
 
         # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -669,7 +672,11 @@ class Model:
                 dim=2,
                 keepdim=True,
             )
-            return torch.clamp(residuals, -thresholds, thresholds)
+            residuals = torch.clamp(residuals, -thresholds, thresholds)
+
+        if self.settings.offload_outputs_to_cpu:
+            residuals = residuals.cpu()
+            empty_cache()
 
         return residuals
 
@@ -681,6 +688,30 @@ class Model:
 
         return torch.cat(residuals, dim=0)
 
+    def get_residuals_mean(self, prompts: list[Prompt]) -> Tensor:
+        if not prompts:
+            raise ValueError("prompts must not be empty")
+
+        running_sum = None
+        total_count = 0
+
+        for batch in batchify(prompts, self.settings.batch_size):
+            batch_residuals = self.get_residuals(batch)
+
+            # Accumulate in high precision on CPU to reduce peak VRAM usage.
+            batch_sum = batch_residuals.sum(dim=0, dtype=torch.float64).cpu()
+
+            if running_sum is None:
+                running_sum = batch_sum
+            else:
+                running_sum += batch_sum
+
+            total_count += batch_residuals.shape[0]
+
+        assert running_sum is not None
+
+        return (running_sum / total_count).to(torch.float32)
+
     # We work with logprobs rather than probabilities for numerical stability
     # when computing the KL divergence.
     def get_logprobs(self, prompts: list[Prompt]) -> Tensor:
@@ -691,6 +722,7 @@ class Model:
             max_new_tokens=1,
             output_scores=True,
             return_dict_in_generate=True,
+            use_cache=False,
         )
 
         # This cast is valid because GenerateDecoderOnlyOutput is the return type
@@ -702,7 +734,15 @@ class Model:
         logits = cast(tuple[FloatTensor], outputs.scores)[0]
 
         # The returned tensor has shape (prompt, token).
-        return F.log_softmax(logits, dim=-1)
+        logprobs = F.log_softmax(logits, dim=-1)
+
+        del outputs
+
+        if self.settings.offload_outputs_to_cpu:
+            logprobs = logprobs.cpu()
+            empty_cache()
+
+        return logprobs
 
     def get_logprobs_batched(self, prompts: list[Prompt]) -> Tensor:
         logprobs = []