[core] Support custom ascendc kernels in vllm-ascend (#233)

This PR add custom ascendc kernel rotary_embedding support in
vllm-ascend, related CMakeLists and setuptools is also added in this PR.

Related: https://github.com/vllm-project/vllm-ascend/issues/156

---------

Signed-off-by: ganyi <pleaplusone.gy@gmail.com>

csrc/torch_binding.cpp

@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2024. 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.
+ */
+
+#include <torch/extension.h>
+#include <torch/library.h>
+#include <torch/version.h>
+#include <torch_npu/csrc/core/npu/NPUStream.h>
+#include <torch_npu/csrc/framework/OpCommand.h>
+#include <torch_npu/csrc/npu/Module.h>
+#include <pybind11/pybind11.h>
+#include "acl/acl.h"
+#include "tiling/platform/platform_ascendc.h"
+#include "aclnn/opdev/platform.h"
+#include "ops.h"
+#include "utils.h"
+
+namespace vllm_ascend {
+
+void rotary_embedding(at::Tensor &positions, at::Tensor &query, at::Tensor &key,
+    int64_t head_size, at::Tensor &cos_sin_cache,  bool is_neox)
+{
+    int32_t deviceId = 0;
+    int64_t num_tokens = positions.numel();
+    int positions_ndim = positions.dim();
+    TORCH_CHECK(
+        positions_ndim == 1 || positions_ndim == 2,
+        "positions must have shape [num_tokens] or [batch_size, seq_len]");
+    if (positions_ndim == 1) {
+      TORCH_CHECK(
+          query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
+          "query, key and positions must have the same number of tokens");
+    }
+    if (positions_ndim == 2) {
+      TORCH_CHECK(
+          query.size(0) == positions.size(0) &&
+              key.size(0) == positions.size(0) &&
+              query.size(1) == positions.size(1) &&
+              key.size(1) == positions.size(1),
+          "query, key and positions must have the same batch_size and seq_len");
+    }
+
+    int query_hidden_size = query.numel() / num_tokens;
+    int key_hidden_size = key.numel() / num_tokens;
+    TORCH_CHECK(query_hidden_size % head_size == 0);
+    TORCH_CHECK(key_hidden_size % head_size == 0);
+
+    // Make sure query and key have consistent number of heads
+    int num_heads = query_hidden_size / head_size;
+    int num_kv_heads = key_hidden_size / head_size;
+    TORCH_CHECK(num_heads % num_kv_heads == 0);
+
+    int rot_dim = cos_sin_cache.size(1);
+    int64_t *position_ids_ptr = positions.data_ptr<int64_t>();
+    void *query_ptr = query.data_ptr();
+    void *key_ptr = key.data_ptr();
+    void *cos_sin_cache_ptr = cos_sin_cache.data_ptr();
+    int64_t query_stride = query.stride(-2);
+    int64_t key_stride = key.stride(-2);
+    at::ScalarType scalar_type = query.scalar_type();
+    aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
+    at_npu::native::OpCommand cmd;
+    cmd.Name("rotary_embedding");
+    cmd.SetCustomHandler([scalar_type, is_neox, num_tokens, stream, position_ids_ptr, 
+                          query_ptr, key_ptr, cos_sin_cache_ptr, rot_dim, query_stride, key_stride,
+                          num_heads, num_kv_heads, head_size]() -> int {
+        auto dtype_num = get_dtype_from_torch(scalar_type);
+        fe::PlatFormInfos platform_infos;
+        int device_id = 0;
+        fe::PlatformInfoManager::GeInstance().GetRuntimePlatformInfosByDevice(device_id, platform_infos);
+        uint32_t aivNum = platform_infos.GetCoreNumByType("aiv");
+        uint32_t loop_cnt = (num_tokens + aivNum - 1) / aivNum;
+        rotary_embedding_impl(dtype_num, is_neox, stream, position_ids_ptr, query_ptr, key_ptr, query_ptr,
+                                key_ptr, cos_sin_cache_ptr, rot_dim, query_stride, key_stride, query_stride,
+                                key_stride, num_heads, num_kv_heads, head_size, num_tokens, loop_cnt, aivNum);
+        return 0;
+    });
+    cmd.Run();
+    return ;
+}
+} // namespace vllm_ascend
+
+TORCH_LIBRARY_EXPAND(_C, ops)
+{
+    // vLLM-Ascend custom ops
+
+    // Rotary embedding
+    // Apply GPT-NeoX style rotary embedding to query and key.
+    ops.def(
+        "rotary_embedding(Tensor positions, Tensor! query,"
+        "                 Tensor! key, int head_size,"
+        "                 Tensor cos_sin_cache, bool is_neox) -> ()");
+    ops.impl("rotary_embedding", torch::kPrivateUse1, &vllm_ascend::rotary_embedding);
+}
+
+REGISTER_EXTENSION(_C)