[PERF] Speedup of MRoPE prepare inputs (#19939)

Signed-off-by: Vadim Gimpelson <[email protected]>

Author: vadiklyutiy

vllm/model_executor/layers/rotary_embedding.py

@@ -26,6 +26,7 @@
 import math
 from typing import Any, Optional, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
@@ -1458,15 +1459,14 @@ def get_next_input_positions(
         ]
 
     @staticmethod
-    def get_next_input_positions_tensor(
-        mrope_position_delta: int,
-        context_len: int,
-        seq_len: int,
-    ) -> torch.Tensor:
-        return torch.arange(
-            mrope_position_delta + context_len,
-            mrope_position_delta + seq_len,
-        ).expand(3, -1)
+    def get_next_input_positions_tensor(out: np.ndarray, out_offset: int,
+                                        mrope_position_delta: int,
+                                        context_len: int, num_new_tokens: int):
+
+        values = np.arange(mrope_position_delta + context_len,
+                           mrope_position_delta + context_len + num_new_tokens,
+                           dtype=out.dtype)
+        out[:, out_offset:out_offset + num_new_tokens] = values
 
     @classmethod
     def omni_get_updates_use_audio_in_video(

vllm/v1/worker/gpu_model_runner.py

@@ -262,6 +262,7 @@ def __init__(
                 dtype=torch.int64,
                 device="cpu",
                 pin_memory=self.pin_memory)
+            self.mrope_positions_np = self.mrope_positions_cpu.numpy()
 
         # Only relevant for models using ALiBi (e.g, MPT)
         self.use_alibi = check_use_alibi(model_config)
@@ -889,15 +890,13 @@ def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
                 dst_start = mrope_pos_ptr
                 dst_end = mrope_pos_ptr + completion_part_len
 
-                self.mrope_positions_cpu[:, dst_start:dst_end] = \
-                    MRotaryEmbedding.get_next_input_positions_tensor(
-                        req.mrope_position_delta,
-                        context_len=num_computed_tokens +
-                        prompt_part_len,
-                        seq_len=num_computed_tokens +
-                        prompt_part_len +
-                        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