#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 "utils.h"
/*
 * How to write a meta implementation for a custom operator (meta kernel):
 *
 * Meta implementations are used for shape and dtype inference, tracing, and export.
 * They do NOT perform any real computation or allocate device memory.
 * Instead, they return empty tensors with the correct shapes, dtypes, and device types.
 *
 * Steps to write a meta implementation:
 * 1. The function signature should match the operator's schema, but only use the arguments
 *    necessary to infer output shapes and dtypes.
 * 2. Use input tensor shapes, dtypes, and any relevant arguments to compute the output shapes.
 * 3. Return empty tensors (e.g., at::empty_symint, at::empty_like) with the correct shape and dtype.
 * 4. Do NOT perform any real computation or data movement.
 * 5. Register the meta implementation with the "Meta" dispatch key using TORCH_LIBRARY_IMPL or similar.
 *
 * Example:
 *   std::tuple<at::Tensor, at::Tensor> my_op_meta(
 *       at::Tensor &input, int64_t some_param) {
 *     // Infer output shape based on input and parameters
 *     auto out_shape = ...;
 *     at::Tensor out = at::empty_symint(out_shape, input.options());
 *     // Return empty tensor(s) with correct shape/dtype
 *     return {out, ...};
 *   }
 *
 * See below for real examples.
 */

namespace vllm_ascend {
namespace meta {

std::tuple<at::Tensor, at::Tensor> rotary_embedding_meta(
  at::Tensor &positions,
  at::Tensor &query,
  at::Tensor &key,
  int64_t head_size, 
  at::Tensor &cos_sin_cache,
  bool is_neox) {
    auto num_tokens = positions.sym_numel();
    auto query_hidden_size = query.sym_numel() / num_tokens;
    auto key_hidden_size = key.sym_numel() / num_tokens;

    auto num_heads = query_hidden_size / head_size;
    auto num_kv_heads = key_hidden_size / head_size;
    at::Tensor query_dst = at::empty_symint({num_tokens, num_heads, head_size}, query.options());
    at::Tensor key_dst = at::empty_symint({num_tokens, num_kv_heads, head_size}, key.options());

    return {query_dst, key_dst};
}

std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask_meta(
    at::Tensor &input,
    const int64_t org_vocab_start_index,
    const int64_t org_vocab_end_index,
    const int64_t num_org_vocab_padding,
    const int64_t added_vocab_start_index,
    const int64_t added_vocab_end_index) {

    at::Tensor masked_input = at::empty_like(input);
    at::Tensor mask = at::empty_like(input, input.options().dtype(at::kBool));

    return {masked_input, mask};
}


} // namespace meta
} // namespace vllm_ascend

namespace {
  // Register the meta implementations of the custom kernels for symbolic tracing, this will also 
  // the custom kernel been captured into aclgraph
  TORCH_LIBRARY_IMPL_EXPAND(_C, Meta, ops) {
    // Rotary embedding meta implementation
    ops.impl("rotary_embedding", &vllm_ascend::meta::rotary_embedding_meta);
    // Masked input and mask meta implementation
    ops.impl("get_masked_input_and_mask", &vllm_ascend::meta::get_masked_input_and_mask_meta);

}
}