[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/worker/neuron_worker.py

@@ -0,0 +1,127 @@
+"""A Neuron worker class."""
+from typing import List, Optional, Tuple
+
+import torch
+import torch.distributed
+
+from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,
+                         ParallelConfig, SchedulerConfig)
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment)
+from vllm.model_executor import set_random_seed
+from vllm.sequence import ExecuteModelRequest
+from vllm.worker.neuron_model_runner import NeuronModelRunner
+from vllm.worker.worker_base import (LocalOrDistributedWorkerBase,
+                                     LoraNotSupportedWorkerBase, WorkerInput)
+
+
+class NeuronWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
+    """A worker class that executes the model on a group of neuron cores.
+    """
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        cache_config: CacheConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+    ) -> None:
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.cache_config = cache_config
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        if self.model_config.trust_remote_code:
+            # note: lazy import to avoid importing torch before initializing
+            from vllm.utils import init_cached_hf_modules
+            init_cached_hf_modules()
+
+        self.model_runner: NeuronModelRunner = NeuronModelRunner(
+            model_config, parallel_config, scheduler_config, device_config)
+        self.is_driver_worker = True
+
+    def init_device(self) -> None:
+        self.init_distributed_environment()
+
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+    def load_model(self):
+        self.model_runner.load_model()
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Determine the number of available KV blocks.
+
+        Swapping is not yet supported, so always return num_cpu_blocks=0.
+
+        We configure num_gpu_blocks to be equal to max_num_seqs.
+        """
+        # Set the number of GPU blocks to be the same as the maximum number of
+        # sequences that can be processed in a single batch. This is equivalent
+        # to schedule without PagedAttention.
+        num_gpu_blocks = self.scheduler_config.max_num_seqs
+
+        # Swap not yet supported with Neuron backend.
+        num_cpu_blocks = 0
+
+        return num_gpu_blocks, num_cpu_blocks
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache.
+        """
+
+        # Different values are not tested.
+        assert num_cpu_blocks == 0
+        assert num_gpu_blocks == self.scheduler_config.max_num_seqs
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+    @property
+    def do_metadata_broadcast(self) -> bool:
+        return False
+
+    @property
+    def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
+        return None
+
+    @torch.inference_mode()
+    def prepare_worker_input(
+            self, execute_model_req: ExecuteModelRequest) -> WorkerInput:
+        return WorkerInput(num_seq_groups=len(
+            execute_model_req.seq_group_metadata_list), )
+
+    def execute_worker(self, worker_input: WorkerInput) -> None:
+        pass
+
+    def get_cache_block_size_bytes(self) -> int:
+        """Determine the size in bytes of a cache block.
+
+        This is required for speculative decoding; it is not yet implemented.
+        """
+        raise NotImplementedError
+
+    def init_distributed_environment(self):
+        """Neuron uses transformers-neuronx for tensor parallelism.
+
+        vLLM still needs the environment inited when TP/PP > 1
+        """
+        init_distributed_environment(
+            world_size=1,
+            rank=self.rank,
+            local_rank=self.local_rank,
+            distributed_init_method=self.distributed_init_method,
+            backend="gloo",
+        )
+        ensure_model_parallel_initialized(
+            1,
+            1,
+        )