wudixzy stack docker build test: 2615-line qwen3_5.py + 12 prebuilt .so + 251-line patch_ops.sh

qwen3_6_scripts/patch_worker_profile_override.py

@@ -0,0 +1,81 @@
+from patch_utils import package_root, replace_one_of
+
+WORKER = package_root("vllm") / "worker" / "worker.py"
+
+CLEAN_BLOCK = """\
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+"""
+
+GUARDED_BLOCK = """\
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model. Mark this synthetic pass so BI100_PROFILE can skip
+        # timing it by default; profiling real requests is the useful signal.
+        _bi100_prev_startup_profile = os.environ.get("BI100_IN_STARTUP_PROFILE")
+        os.environ["BI100_IN_STARTUP_PROFILE"] = "1"
+        try:
+            self.model_runner.profile_run()
+        finally:
+            if _bi100_prev_startup_profile is None:
+                os.environ.pop("BI100_IN_STARTUP_PROFILE", None)
+            else:
+                os.environ["BI100_IN_STARTUP_PROFILE"] = _bi100_prev_startup_profile
+"""
+
+NEW_BLOCK = """\
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+
+        # BI100: Qwen3.6 batched dummy profile_run can trip GDN non-finite
+        # checks before the server starts. If the operator explicitly provides
+        # --num-gpu-blocks-override, trust that conservative capacity value and
+        # skip only the synthetic profile pass. Real inference still uses the
+        # normal GDN fail-fast path.
+        if self.cache_config.num_gpu_blocks_override is not None:
+            cache_block_size = self.get_cache_block_size_bytes()
+            if cache_block_size == 0:
+                num_cpu_blocks = 0
+            else:
+                num_cpu_blocks = int(self.cache_config.swap_space_bytes //
+                                     cache_block_size)
+            logger.warning(
+                "[BI100] skipping worker.profile_run because "
+                "num_gpu_blocks_override=%d was explicitly set",
+                self.cache_config.num_gpu_blocks_override)
+            gc.collect()
+            torch.cuda.empty_cache()
+            return self.cache_config.num_gpu_blocks_override, max(num_cpu_blocks, 0)
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model. Mark this synthetic pass so BI100_PROFILE can skip
+        # timing it by default; profiling real requests is the useful signal.
+        _bi100_prev_startup_profile = os.environ.get("BI100_IN_STARTUP_PROFILE")
+        os.environ["BI100_IN_STARTUP_PROFILE"] = "1"
+        try:
+            self.model_runner.profile_run()
+        finally:
+            if _bi100_prev_startup_profile is None:
+                os.environ.pop("BI100_IN_STARTUP_PROFILE", None)
+            else:
+                os.environ["BI100_IN_STARTUP_PROFILE"] = _bi100_prev_startup_profile
+"""
+
+replace_one_of(
+    WORKER,
+    [
+        (GUARDED_BLOCK, NEW_BLOCK),
+        (CLEAN_BLOCK, NEW_BLOCK),
+    ],
+    required=True,
+    already_contains="[BI100] skipping worker.profile_run",
+)