Add Custom Kernels For LoRA Performance (#1884)

### What this PR does / why we need it?
Add two custom kernels(bgmv_shrink and bgmv expand) to solve the
performance of LoRA
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file to test the custom ascendc kernel. See
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py and
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py
Based on the actual test of the QWen2.5 7B model using vllm-ascend
version v0.9.2.rc1, the TTFT, TPOT and throughput have increased by
about 70%.

- vLLM version: v0.9.2
- vLLM main:
40d86ee412

---------

Signed-off-by: taoxudonghaha <justsheldon@163.com>

vllm_ascend/lora/punica_wrapper/lora_ops.py

@@ -0,0 +1,112 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+
+def bgmv_shrink(inputs: torch.Tensor,
+                lora_a_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                scaling: float = 1.0):
+    return torch.ops._C.bgmv_shrink(
+        inputs,
+        lora_a_weights,
+        lora_indices_tensor,
+        output_tensor,
+        scaling,
+    )
+
+
+def bgmv_expand(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                add_inputs: bool = True):
+    return torch.ops._C.bgmv_expand(
+        inputs,
+        lora_b_weights,
+        lora_indices_tensor,
+        output_tensor,
+        0,
+        output_tensor.size(1),
+    )
+
+
+def bgmv_expand_slice(inputs: torch.Tensor,
+                      lora_b_weights: torch.Tensor,
+                      output_tensor: torch.Tensor,
+                      lora_indices_tensor: torch.Tensor,
+                      slice_offset: int,
+                      slice_size: int,
+                      add_inputs: bool = True):
+    return torch.ops._C.bgmv_expand(inputs, lora_b_weights,
+                                    lora_indices_tensor, output_tensor,
+                                    slice_offset, slice_size)
+
+
+def sgmv_shrink(
+    inputs: torch.Tensor,
+    lora_a_weights: torch.Tensor,
+    output_tensor: torch.Tensor,
+    b_seq_start_loc: torch.Tensor,
+    seq_len_tensor: torch.Tensor,
+    lora_indices_tensor: torch.Tensor,
+    batches: int,
+    max_seq_length: int,
+    token_nums: int,
+    scaling: float,
+):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_shrink(inputs, lora_a_weights, output_tensor, exploded_indices,
+                scaling)
+
+
+def sgmv_expand(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                b_seq_start_loc: torch.Tensor,
+                seq_len_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                batches: int,
+                max_seq_length: int,
+                token_nums: int,
+                add_inputs: bool = False):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_expand(inputs, lora_b_weights, output_tensor, exploded_indices,
+                add_inputs)
+
+
+def sgmv_expand_slice(inputs: torch.Tensor,
+                      lora_b_weights: torch.Tensor,
+                      output_tensor: torch.Tensor,
+                      b_seq_start_loc: torch.Tensor,
+                      seq_len_tensor: torch.Tensor,
+                      lora_indices_tensor: torch.Tensor,
+                      batches: int,
+                      max_seq_length: int,
+                      token_nums: int,
+                      slice_offset: int,
+                      slice_size: int,
+                      add_inputs: bool = False):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_expand_slice(inputs, lora_b_weights, output_tensor, exploded_indices,
+                      slice_offset, slice_size, add_inputs)