[V1] Simplify M-RoPE (#12352)

Signed-off-by: Roger Wang <ywang@roblox.com>
Co-authored-by: imkero <kerorek@outlook.com>

vllm/v1/worker/gpu_model_runner.py

@@ -144,29 +144,25 @@ class GPUModelRunner:
 
         # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
         if self.model_config.uses_mrope:
-            # NOTE: `mrope_positions` is implemented as a permuted tensor to
-            # satisfy the following properties to allow `torch.compile` to work
-            # properly:
-            # - shape: (3, <variable>)
-            # - stride: (1, 3)
-            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1921022256
+            # NOTE: `mrope_positions` is implemented with one additional dummy
+            # position on purpose to make it non-contiguous so that it can work
+            # with torch compile.
+            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
 
             # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
             # the modality of inputs. For text-only inputs, each dimension has
             # identical position IDs, making M-RoPE functionally equivalent to
             # 1D-RoPE.
             # See page 5 of https://arxiv.org/abs/2409.12191
-            self.mrope_positions = torch.zeros((self.max_num_tokens, 3),
+            self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
                                                dtype=torch.int64,
                                                device=self.device)
-            self.mrope_positions_cpu = torch.zeros((self.max_num_tokens, 3),
+            self.mrope_positions_cpu = torch.zeros(
+                (3, self.max_num_tokens + 1),
                 dtype=torch.int64,
                 device="cpu",
                 pin_memory=self.pin_memory)
 
-            self.mrope_positions = self.mrope_positions.permute((1, 0))
-            self.mrope_positions_cpu = self.mrope_positions_cpu.permute((1, 0))
-
         self.inputs_embeds = torch.zeros(
             (self.max_num_tokens, self.hidden_size),
             dtype=self.dtype,