[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/prompt_adapter/layers.py

@@ -0,0 +1,80 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from torch import nn
+
+from vllm.adapter_commons.layers import AdapterMapping
+from vllm.config import PromptAdapterConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+
+
+@dataclass
+class PromptAdapterMapping(AdapterMapping):
+    pass
+
+
+class VocabParallelEmbeddingWithPromptAdapter(nn.Module):
+
+    def __init__(self, base_layer: VocabParallelEmbedding) -> None:
+        super().__init__()
+        self.base_layer = base_layer
+        self.emb_layer = self.base_layer
+        if 'LoRA' in base_layer.__class__.__name__:
+            self.emb_layer = self.base_layer.base_layer
+
+    def create_prompt_adapter_weights(
+            self, prompt_adapter_config: PromptAdapterConfig):
+        self.embeddings_tensors = torch.zeros(
+            (
+                prompt_adapter_config.max_prompt_adapters,
+                prompt_adapter_config.max_prompt_adapter_token,
+                self.emb_layer.embedding_dim,
+            ),
+            dtype=self.emb_layer.weight.dtype,
+            device=self.emb_layer.weight.device,
+        )
+        self.adapter_lengths = torch.zeros(
+            prompt_adapter_config.max_prompt_adapters,
+            dtype=torch.long,
+            device=self.emb_layer.weight.device)
+
+        self.indices_gpu: torch.Tensor
+        self.embedding_indices_gpu: torch.Tensor
+
+    def reset_prompt_adapter(self, index: int):
+        self.embeddings_tensors[index] = 0
+
+    def set_prompt_adapter(
+        self,
+        index: int,
+        adapter_model: Optional[torch.Tensor],
+    ):
+        self.reset_prompt_adapter(index)
+        if adapter_model is not None:
+            length = adapter_model.shape[0]
+            self.embeddings_tensors[index, :length] = adapter_model
+            self.adapter_lengths[index] = length
+
+    def set_mapping(
+        self,
+        prompt_indices: torch.Tensor,
+        prompt_embedding_indices: torch.Tensor,
+    ):
+        self.indices_gpu = prompt_indices.to(
+            device=self.emb_layer.weight.device)
+        self.embedding_indices_gpu = prompt_embedding_indices.to(
+            device=self.emb_layer.weight.device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.base_layer(x)
+        if self.embedding_indices_gpu.ndim > 1:
+            valid_mask = self.indices_gpu != -1
+            gathered_embeddings = self.embeddings_tensors[
+                self.embedding_indices_gpu[:, 0],
+                self.embedding_indices_gpu[:, 1]]
+
+            # Update hidden states
+            hidden_states[valid_mask] = gathered_embeddings
+        return hidden_states