删除1d_mrope

vllm/envs.py

@@ -157,7 +157,6 @@ 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
@@ -1926,8 +1925,6 @@ 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

@@ -398,7 +398,6 @@ class GPUModelRunner(
         # Multi-modal data support
         self.mm_registry = MULTIMODAL_REGISTRY
         self.uses_mrope = model_config.uses_mrope
-        self.use_1d_mrope = self.uses_mrope and envs.VLLM_1D_MROPE
         self.uses_xdrope_dim = model_config.uses_xdrope_dim
         self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
             model_config
@@ -614,15 +613,9 @@ class GPUModelRunner(
             # identical position IDs, making M-RoPE functionally equivalent to
             # 1D-RoPE.
             # See page 5 of https://arxiv.org/abs/2409.12191
-            if self.use_1d_mrope:
-                self.mrope_positions = self._make_buffer(
-                    3 * (self.max_num_tokens + 1), dtype=torch.int64
-                )
-            else:
-                self.mrope_positions = self._make_buffer(
-                    (3, self.max_num_tokens + 1), dtype=torch.int64
-                )
-
+            self.mrope_positions = self._make_buffer(
+                (3, self.max_num_tokens + 1), dtype=torch.int64
+            )
 
         # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
         if self.uses_xdrope_dim > 0:
@@ -778,18 +771,12 @@ class GPUModelRunner(
     def _get_positions(self, num_tokens: Any):
         if isinstance(num_tokens, int):
             if self.uses_mrope:
-                if self.use_1d_mrope:
-                    return self.mrope_positions.gpu[: 3 * num_tokens].view(
-                        num_tokens, 3
-                    ).T
                 return self.mrope_positions.gpu[:, :num_tokens]
             if self.uses_xdrope_dim > 0:
                 return self.xdrope_positions.gpu[:, :num_tokens]
             return self.positions.gpu[:num_tokens]
         else:
             if self.uses_mrope:
-                if self.use_1d_mrope:
-                    return self.mrope_positions.gpu.view(-1, 3)[num_tokens].T
                 return self.mrope_positions.gpu[:, num_tokens]
             if self.uses_xdrope_dim > 0:
                 return self.xdrope_positions.gpu[:, num_tokens]
@@ -806,17 +793,10 @@ 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,
@@ -825,7 +805,7 @@ class GPUModelRunner(
     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,
@@ -2131,13 +2111,6 @@ 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
@@ -2164,14 +2137,9 @@ 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
-                    ]
+                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
+                    :, src_start:src_end
+                ]
                 mrope_pos_ptr += prompt_part_len
 
             if completion_part_len > 0:
@@ -2180,26 +2148,13 @@ 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,
-                        mrope_position_delta=req.mrope_position_delta,
-                        context_len=num_computed_tokens + prompt_part_len,
-                        num_new_tokens=completion_part_len,
-                    )
+                MRotaryEmbedding.get_next_input_positions_tensor(
+                    out=self.mrope_positions.np,
+                    out_offset=dst_start,
+                    mrope_position_delta=req.mrope_position_delta,
+                    context_len=num_computed_tokens + prompt_part_len,
+                    num_new_tokens=completion_part_len,
+                )
 
                 mrope_pos_ptr += completion_part_len