feat: opt-in torch.compile of the code predictor loop (TTS_COMPILE_PREDICTOR=1)

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

Dockerfile

@@ -24,8 +24,8 @@ RUN git clone https://github.com/andimarafioti/faster-qwen3-tts.git /app && \
     cd /app && git checkout --detach ${FASTER_QWEN3_TTS_REF}
 
 # Apply DGX Spark patches (non_streaming_mode=True, per-voice temperature/top_k/top_p)
-COPY patches/openai_server.patch /tmp/
-RUN cd /app && git apply /tmp/openai_server.patch
+COPY patches/openai_server.patch patches/predictor_compile.patch /tmp/
+RUN cd /app && git apply /tmp/openai_server.patch && git apply /tmp/predictor_compile.patch
 
 # Create virtual environment (Ubuntu 24.04 enforces PEP 668)
 ENV VIRTUAL_ENV=/opt/venv

patches/predictor_compile.patch

@@ -0,0 +1,25 @@
+diff --git a/faster_qwen3_tts/predictor_graph.py b/faster_qwen3_tts/predictor_graph.py
+index 38a52ca..70851eb 100644
+--- a/faster_qwen3_tts/predictor_graph.py
++++ b/faster_qwen3_tts/predictor_graph.py
+@@ -13,6 +13,8 @@ Strategy:
+ - Unroll the full 15-step loop for deterministic shapes
+ - Capture the entire loop as a single CUDA graph
+ """
++import os
++
+ import torch
+ from transformers import StaticCache
+ from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+@@ -175,6 +177,11 @@ class PredictorGraph:
+         self._init_cache_layers()
+         self._build_attention_masks()
+ 
++        # Opt-in: fuse the 15-step predictor loop with Inductor before graph capture.
++        # ~17% faster streaming on GB10 (0.6B); adds ~1 min to the first warmup.
++        if os.environ.get("TTS_COMPILE_PREDICTOR") == "1":
++            self._full_loop = torch.compile(self._full_loop, dynamic=False)
++
+         for _ in range(num_warmup):
+             self.static_cache.reset()
+             self._full_loop()