project_8/ex_engine/xllm_layers/common/rotary_embedding.h

/* Copyright 2025 The xLLM Authors. 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

    https://github.com/jd-opensource/xllm/blob/main/LICENSE

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.
==============================================================================*/

#pragma once

#include <torch/torch.h>
#include <torch/types.h>

#include <memory>

#include "attention_metadata.h"
#include "core/framework/model_context.h"
#include "framework/model/model_args.h"
#include "rotary_embedding_util.h"

namespace xllm {
namespace layer {

class RotaryEmbeddingBase : public torch::nn::Module {
 public:
  ~RotaryEmbeddingBase() override = default;

  virtual void forward(torch::Tensor& input,
                       const torch::Tensor& positions,
                       const torch::Tensor& cu_query_lens,
                       int64_t max_query_len,
                       bool is_prompt) = 0;
  virtual const torch::Tensor& get_sin_cache() const = 0;
  virtual const torch::Tensor& get_cos_cache() const = 0;
  virtual const bool get_interleaved() const = 0;
};

class RotaryEmbeddingImpl : public RotaryEmbeddingBase {
 public:
  RotaryEmbeddingImpl(int64_t rotary_dim,
                      int64_t max_position_embeddings,
                      int64_t rope_theta,
                      bool interleaved,
                      const torch::TensorOptions& options);
  RotaryEmbeddingImpl(const ModelContext& context);

  void forward(torch::Tensor& q,
               torch::Tensor& k,
               const torch::Tensor& positions,
               const torch::Tensor& cu_query_lens,
               int64_t max_query_len,
               bool is_prompt);
  // Single tensor forward for MLA architecture
  void forward(torch::Tensor& input,
               const torch::Tensor& positions,
               const torch::Tensor& cu_query_lens,
               int64_t max_query_len,
               bool is_prompt) override;

  const torch::Tensor& precomputed_cos_sin_cache() {
    return precomputed_cos_sin_cache_;
  }

  torch::Tensor get_cos_sin_cache() { return cos_sin_cache_; }
  const torch::Tensor& get_sin_cache() const override { return sin_; }
  const torch::Tensor& get_cos_cache() const override { return cos_; }
  const bool get_interleaved() const override { return interleaved_; }

 protected:
  bool interleaved_;
  torch::Tensor cos_sin_cache_;
  // Pre-formatted [cos_half, sin_half] cache for CUDA/MUSA/ILU kernels.
  // Avoids chunk/cat operations on every forward call.
  torch::Tensor precomputed_cos_sin_cache_;

 private:
  torch::Tensor sin_;
  torch::Tensor cos_;
};
TORCH_MODULE(RotaryEmbedding);

class MRotaryEmbeddingImpl : public RotaryEmbeddingImpl {
 public:
  MRotaryEmbeddingImpl(int64_t rotary_dim,
                       int64_t max_position_embeddings,
                       int64_t rope_theta,
                       bool interleaved,
                       const std::vector<int64_t>& rope_scaling_mrope_section,
                       const torch::TensorOptions& options);

  void forward(torch::Tensor& q,
               torch::Tensor& k,
               const torch::Tensor& positions,
               const AttentionMetadata& attn_metadata);

 private:
  std::vector<int64_t> mrope_section_;
  torch::Tensor mrope_cu_seq_lens_;
};
TORCH_MODULE(MRotaryEmbedding);

class DeepseekScalingRotaryEmbeddingImpl : public RotaryEmbeddingBase {
 public:
  DeepseekScalingRotaryEmbeddingImpl(
      int64_t head_size,
      int64_t rotary_dim,
      int64_t max_position_embeddings,
      int64_t rope_scaling_original_max_position_embeddings,
      int64_t rope_theta,
      bool interleaved,
      float scaling_factor,
      float extrapolation_factor,
      float attn_factor,
      float beta_fast,
      float beta_slow,
      float mscale,
      float mscale_all_dim,
      const torch::TensorOptions& options);

  void forward(torch::Tensor& input,
               const torch::Tensor& positions,
               const torch::Tensor& cu_query_lens,
               int64_t max_query_len,
               bool is_prompt) override;
  const torch::Tensor& get_sin_cache() const override { return sin_; }
  const torch::Tensor& get_cos_cache() const override { return cos_; }
  const bool get_interleaved() const override { return interleaved_; }

 private:
  int64_t head_size_;
  int64_t rotary_dim_;
  bool interleaved_;
  torch::Tensor sin_;
  torch::Tensor cos_;
  torch::Tensor cos_sin_cache_;
  // Pre-formatted [cos_half, sin_half] cache for CUDA/MUSA/ILU kernels.
  // Avoids chunk/cat operations on every forward call.
  torch::Tensor precomputed_cos_sin_cache_;
};
TORCH_MODULE(DeepseekScalingRotaryEmbedding);

// Factory function: creates the appropriate RoPE type based on model args
std::shared_ptr<RotaryEmbeddingBase> create_mla_rotary_embedding(
    const ModelArgs& args,
    int64_t rotary_dim,
    int64_t max_position_embeddings,
    bool interleaved,
    const torch::TensorOptions& options);

}  // namespace layer
}  // namespace xllm