vl-mrope-1d优化

vllm/envs.py

@@ -156,6 +156,7 @@ if TYPE_CHECKING:
     VLLM_MXFP4_USE_MARLIN: bool | None = None
     VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
     VLLM_V1_USE_OUTLINES_CACHE: bool = False
+    VLLM_1D_MROPE: bool = False
     VLLM_TPU_BUCKET_PADDING_GAP: int = 0
     VLLM_TPU_MOST_MODEL_LEN: int | None = None
     VLLM_TPU_USING_PATHWAYS: bool = False
@@ -1888,6 +1889,8 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_MOE_W16A16_TRITON":
         lambda: (os.environ.get("VLLM_USE_MOE_W16A16_TRITON", "0").lower() in
                  ("true", "1")),
+    "VLLM_1D_MROPE":
+        lambda: (os.environ.get("VLLM_1D_MROPE", "0").lower() in ("true", "1")),
     #If set to 1/True, enable the V1 fast token-id copy path in InputBatch.
     "VLLM_V1_FAST_TOKEN_ID_COPY":
         lambda: (os.environ.get("VLLM_V1_FAST_TOKEN_ID_COPY", "False").lower() in

vllm/v1/worker/gpu_model_runner.py

@@ -789,6 +789,30 @@ class GPUModelRunner(
             pin_memory=self.pin_memory,
             with_numpy=numpy,
         )
+    def _copy_mrope_positions_to_gpu(self, num_tokens: int) -> None:
+        if not self.uses_mrope:
+            return
+        if self.use_1d_mrope:
+            num_values = 3 * num_tokens
+            self.mrope_positions.gpu[:num_values].copy_(
+                self.mrope_positions.cpu[:num_values],
+                non_blocking=True,
+            )
+            return
+        
+        self.mrope_positions.gpu[:, :num_tokens].copy_(
+            self.mrope_positions.cpu[:, :num_tokens],
+            non_blocking=True,
+        )
+
+    def _copy_xdrope_positions_to_gpu(self, num_tokens: int) -> None:
+        if self.uses_xdrope_dim <= 0:
+            return
+        
+        self.xdrope_positions.gpu[:, :num_tokens].copy_(
+            self.xdrope_positions.cpu[:, :num_tokens],
+            non_blocking=True,
+        )
 
     def _init_model_kwargs(self):
         model_kwargs = dict[str, Any]()
@@ -1592,19 +1616,12 @@ class GPUModelRunner(
             total_num_scheduled_tokens,
             cu_num_tokens,
         )
-
         if self.uses_mrope:
             # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
-                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
-                non_blocking=True,
-            )
+            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
         elif self.uses_xdrope_dim > 0:
             # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
-            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
-                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
-                non_blocking=True,
-            )
+            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
         else:
             # Common case (1D positions)
             self.positions.copy_to_gpu(total_num_scheduled_tokens)
@@ -2047,6 +2064,13 @@ class GPUModelRunner(
 
     def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
         mrope_pos_ptr = 0
+        if self.use_1d_mrope:
+            mrope_positions_token_major = self.mrope_positions.cpu.view(
+                self.max_num_tokens + 1, 3
+            )
+            mrope_positions_token_major_np = self.mrope_positions.np.reshape(
+                self.max_num_tokens + 1, 3
+            )
         for index, req_id in enumerate(self.input_batch.req_ids):
             req = self.requests[req_id]
             assert req.mrope_positions is not None
@@ -2073,6 +2097,11 @@ class GPUModelRunner(
                 src_start = num_computed_tokens
                 src_end = num_computed_tokens + prompt_part_len
 
+                if self.use_1d_mrope:
+                    mrope_positions_token_major[dst_start:dst_end, :].copy_(
+                        req.mrope_positions[:, src_start:src_end].transpose(0, 1)
+                    )
+                else:
                     self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                         :, src_start:src_end
                     ]
@@ -2084,6 +2113,19 @@ class GPUModelRunner(
                 dst_end = mrope_pos_ptr + completion_part_len
 
                 assert req.mrope_position_delta is not None
+                if self.use_1d_mrope:
+                    values = np.arange(
+                        req.mrope_position_delta + num_computed_tokens + prompt_part_len,
+                        req.mrope_position_delta
+                        + num_computed_tokens
+                        + prompt_part_len
+                        + completion_part_len,
+                        dtype=mrope_positions_token_major_np.dtype,
+                    )
+                    mrope_positions_token_major_np[dst_start:dst_end, :] = values[
+                        :, None
+                    ]
+                else:
                     MRotaryEmbedding.get_next_input_positions_tensor(
                         out=self.mrope_positions.np,
                         out_offset=dst_start,
@@ -2574,11 +2616,11 @@ class GPUModelRunner(
 
         if should_sync_mrope_positions:
             self._calc_mrope_positions(scheduler_output)
-            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
+            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
 
         if should_sync_xdrope_positions:
             self._calc_xdrope_positions(scheduler_output)
-            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)
+            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
 
         return mm_embeds, is_mm_embed
 
@@ -2837,12 +2879,7 @@ class GPUModelRunner(
             inputs_embeds = None
             model_kwargs = self._init_model_kwargs()
 
-        if self.uses_mrope:
-            positions = self.mrope_positions.gpu[:, :num_input_tokens]
-        elif self.uses_xdrope_dim > 0:
-            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
-        else:
-            positions = self.positions.gpu[:num_input_tokens]
+        positions = self._get_positions(num_input_tokens)
 
         if is_first_rank:
             intermediate_tensors = None
@@ -4727,13 +4764,13 @@ class GPUModelRunner(
                 input_ids = self.input_ids.gpu[:num_tokens_padded]
                 inputs_embeds = None
 
-            if self.uses_mrope:
-                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
-            elif self.uses_xdrope_dim > 0:
-                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
-            else:
-                positions = self.positions.gpu[:num_tokens_padded]
-
+            # if self.uses_mrope:
+            #     positions = self.mrope_positions.gpu[:, :num_tokens_padded]
+            # elif self.uses_xdrope_dim > 0:
+            #     positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
+            # else:
+            #     positions = self.positions.gpu[:num_tokens_padded]
+            positions = self._get_positions(num_tokens_padded)
             if get_pp_group().is_first_rank:
                 intermediate_tensors = None
             else: