[DEPLOY] Complete submission: baseline + all optimizations

Adds ALL files needed for Dockerfile build:
  - qwen3_6_scripts/ (baseline patches + our optimizations)
  - vllm/ (full vllm package)
  - paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
  - Dockerfile + computility-run.yaml

Our optimizations vs baseline:
  1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
     Triton try/fallback, V2 heuristic, threshold 32K→64K
  2. paged_attention_v2_pytorch.py: fills NotImplementedError,
     single-bmm Phase 1 (195 launches → 3)
  3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
  4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
  5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
     max-num-batched-tokens 8192→16384

This repo can now be submitted to dev.modelhub.org.cn as-is.

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -0,0 +1,67 @@
+import os
+from importlib.util import find_spec
+from typing import Any, Dict, List, Optional
+
+from torch.nn import Module
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+SUPPORTED_QUANT_DTYPE_LIST = ['s8', 'f8e4m3fn']
+
+
+class NeuronQuantConfig(QuantizationConfig):
+    """Int8 Quantization Config class for Neuron Backend."""
+
+    def __init__(
+        self,
+        dequant_dtype: str = "f16",
+        quantize_method: str = "vector_dynamic",
+    ) -> None:
+        self.quant_dtype = os.getenv("NEURON_QUANT_DTYPE", "s8")
+        if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
+            raise ValueError(
+                f"Neuron quantization datatype {self.quant_dtype} is not valid,"
+                f"the quantization datatype should match one of the below types"
+                f"{SUPPORTED_QUANT_DTYPE_LIST}")
+        self.dequant_dtype = dequant_dtype
+        self.quantize_method = quantize_method
+
+    def get_name(self) -> str:
+        return "neuron_quant"
+
+    def get_supported_act_dtypes(self) -> List[str]:
+        return SUPPORTED_QUANT_DTYPE_LIST
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "This function should not be called with Neuron Backend")
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "NeuronQuantConfig":
+        quantize_method = cls.get_from_keys(config, ["quantize_method"])
+        dequant_dtype = cls.get_from_keys(config, ["dequant_dtype"])
+        return cls(dequant_dtype=dequant_dtype,
+                   quantize_method=quantize_method)
+
+    def get_quant_method(self, layer: Module, prefix: str) -> Optional[Any]:
+        if find_spec("transformers_neuronx") is not None:
+            return self.get_quantization_config()
+        else:
+            raise NotImplementedError(
+                "Neuron Quantization is only supported through"
+                " transformers_neuronx.")
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_quantization_config(self):
+        from transformers_neuronx.config import QuantizationConfig
+        return QuantizationConfig(quant_dtype=self.quant_dtype,
+                                  dequant_dtype=self.dequant_dtype,
+                                  quantize_method=self.quantize_method)