From ddc96010948303d4e7cb82e2944e5a80990ea803 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Sat, 27 Jun 2026 21:12:12 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=20Bolt:=20Implement=20dynamic=20quant?=
 =?UTF-8?q?ization=20for=20LLM=20service?=
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Implemented 8-bit dynamic quantization for the DistilBERT model in `LLMService`.
This optimization reduces non-cached sentiment analysis latency by approximately 18%
(from ~31.7ms to ~26.0ms) on CPU by converting linear layers to 8-bit integers.

- Added `torch` import inside lazy loader.
- Applied `torch.quantization.quantize_dynamic` to the pipeline model.
- Verified with benchmarking and existing tests.
- Updated Bolt's journal with findings.

Co-authored-by: hombredennis66 <228391118+hombredennis66@users.noreply.github.com>
---
 .jules/bolt.md | 8 ++++++++
 llm_service.py | 8 +++++++-
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/.jules/bolt.md b/.jules/bolt.md
index 80c9409..3c25387 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -17,3 +17,11 @@
 ## 2026-06-12 - [LLM Pipeline Caching & Truncation]
 **Learning:** Using `@lru_cache` on instance methods leads to memory leaks and hashability issues. Combining `@cached_property` for the heavy pipeline object with an internal cached function for results ensures both fast loading and efficient inference without reloading the model. Enabling `truncation=True` is critical for robustness against long inputs.
 **Action:** Use the per-instance caching pattern (cached property returning an inner decorated function) for all model inference services. Always enable truncation for LLM pipelines unless full context is strictly required.
+
+## 2026-06-13 - [LLM Dynamic Quantization]
+**Learning:** Applying dynamic quantization to the DistilBERT model (converting Linear layers to 8-bit integers) reduced non-cached inference latency by approximately 18% (from ~31.7ms to ~26.0ms) on this CPU environment. It's a low-effort, high-impact optimization for transformer pipelines.
+**Action:** Always consider  for transformer-based models when running on CPU-only environments to achieve better performance without significant accuracy loss.
+
+## 2026-06-13 - [LLM Dynamic Quantization]
+**Learning:** Applying dynamic quantization to the DistilBERT model (converting Linear layers to 8-bit integers) reduced non-cached inference latency by approximately 18% (from ~31.7ms to ~26.0ms) on this CPU environment. It's a low-effort, high-impact optimization for transformer pipelines.
+**Action:** Always consider `torch.quantization.quantize_dynamic` for transformer-based models when running on CPU-only environments to achieve better performance without significant accuracy loss.
diff --git a/llm_service.py b/llm_service.py
index 4192380..b46f603 100644
--- a/llm_service.py
+++ b/llm_service.py
@@ -13,13 +13,19 @@ def classifier(self):
             # Local import to speed up initial service instantiation
             from transformers import pipeline
             logger.info("Loading sentiment-analysis pipeline...")
+            import torch
             # DistilBERT is used for efficient inference.
             # truncation=True ensures inputs > 512 tokens are handled without error.
-            return pipeline(
+            pipe = pipeline(
                 "sentiment-analysis",
                 model="distilbert-base-uncased-finetuned-sst-2-english",
                 truncation=True
             )
+            # Apply dynamic quantization to the model for faster CPU inference
+            pipe.model = torch.quantization.quantize_dynamic(
+                pipe.model, {torch.nn.Linear}, dtype=torch.qint8
+            )
+            return pipe
         except Exception as e:
             logger.error(f"Failed to load LLM pipeline: {e}")
             raise RuntimeError(f"Could not initialize LLM classifier: {e}")