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Merged
whjthu merged 1 commit into from
Jan 16, 2026
Merged

issue/920 RoPE supports longrope #921

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56 changes: 50 additions & 6 deletions include/infinicore/nn/rope.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,47 @@ class RoPE : public Module {
GPT_NEOX = 1, // GPT-NeoX style RoPE algorithm (First half dimensions for sin, second half for cos)
};

enum class ScalingType {
DEFAULT = 0, // Default RoPE
LONGROPE = 1 // Long-RoPE
};

class ScalingConfig {
public:
virtual ~ScalingConfig() = default;
ScalingType type() const { return type_; }

protected:
ScalingType type_ = ScalingType::DEFAULT;
ScalingConfig(ScalingType type) : type_(type) {}
};

// longrope scaling
class LongRopeConfig : public ScalingConfig {
protected:
std::vector<float> short_factor_;
std::vector<float> long_factor_;
size_t original_max_position_embeddings_;
float factor_;

public:
LongRopeConfig(
std::vector<float> short_factor,
std::vector<float> long_factor,
size_t original_max_position_embeddings,
float factor = 1.0f)
: ScalingConfig(ScalingType::LONGROPE),
short_factor_(short_factor),
long_factor_(long_factor),
original_max_position_embeddings_(original_max_position_embeddings),
factor_(factor == 1.0f ? 1.0f : std::sqrt(1 + std::log(factor) / std::log(original_max_position_embeddings))) {}
~LongRopeConfig() override = default;
size_t original_max_position_embeddings() const { return original_max_position_embeddings_; }
const std::vector<float> &short_factor() const { return short_factor_; }
const std::vector<float> &long_factor() const { return long_factor_; }
float factor() const { return factor_; }
};

/**
* @brief Construct a RoPE layer
*
Expand All @@ -26,13 +67,15 @@ class RoPE : public Module {
* @param algo RoPE algorithm type (default: Algo::GPT_J)
* @param dtype Data type for sin/cos cache (default: DataType::F32)
* @param device Device to create the cache on
* @param scaling RoPE scaling type (default: nullptr)
*/
RoPE(size_t head_dim,
size_t max_seq_len,
double theta = 10000.0,
Algo algo = Algo::GPT_J,
const DataType &dtype = DataType::F32,
const Device &device = Device());
const Device &device = Device(),
std::shared_ptr<ScalingConfig> scaling = nullptr);

/**
* @brief Forward pass: apply RoPE to a tensor
Expand Down Expand Up @@ -88,11 +131,12 @@ class RoPE : public Module {
private:
void initialize_cache();

size_t head_dim_; // Dimension of each attention head
size_t max_seq_len_; // Maximum sequence length
double theta_; // Base frequency for rotary embeddings
Algo algo_; // RoPE algorithm type
DataType dtype_; // Data type for cache tables
size_t head_dim_; // Dimension of each attention head
size_t max_seq_len_; // Maximum sequence length
double theta_; // Base frequency for rotary embeddings
Algo algo_; // RoPE algorithm type
DataType dtype_; // Data type for cache tables
std::shared_ptr<ScalingConfig> scaling_; // RoPE scaling type
};

} // namespace infinicore::nn
28 changes: 23 additions & 5 deletions src/infinicore/nn/rope.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,14 @@ RoPE::RoPE(size_t head_dim,
double theta,
Algo algo,
const DataType &dtype,
const Device &device)
const Device &device,
std::shared_ptr<ScalingConfig> scaling)
: head_dim_(head_dim),
max_seq_len_(max_seq_len),
theta_(theta),
algo_(algo),
dtype_(dtype) {
dtype_(dtype),
scaling_(scaling) {
if (head_dim % 2 != 0) {
throw std::invalid_argument("head_dim must be even for RoPE, got " + std::to_string(head_dim));
}
Expand Down Expand Up @@ -54,14 +56,30 @@ void RoPE::initialize_cache() {
for (size_t j = 0; j < cache_dim; j++) {
// GPT-J style inverse frequency: theta^(-2j/head_dim)
// Compute directly in float to avoid double->float casting
float inv_freq = 1.0f / std::pow(static_cast<float>(theta_), 2.0f * static_cast<float>(j) / static_cast<float>(head_dim_));
float inv_freq;
float table_factor = 1.0f;
if (scaling_ == nullptr) {
inv_freq = 1.0f / std::pow(static_cast<float>(theta_), 2.0f * static_cast<float>(j) / static_cast<float>(head_dim_));
} else if (scaling_->type() == ScalingType::LONGROPE) {
std::shared_ptr<LongRopeConfig> lr = std::dynamic_pointer_cast<LongRopeConfig>(scaling_);
table_factor = lr->factor();
float _ext;
if (pos < lr->original_max_position_embeddings()) {
_ext = lr->short_factor()[j];
} else {
_ext = lr->long_factor()[j];
}
inv_freq = 1.0f / (_ext * std::pow(static_cast<float>(theta_), 2.0f * static_cast<float>(j) / static_cast<float>(head_dim_)));
} else {
inv_freq = 1.0f / std::pow(static_cast<float>(theta_), 2.0f * static_cast<float>(j) / static_cast<float>(head_dim_));
}

// Compute angle: position * inverse_frequency
float angle = static_cast<float>(pos) * inv_freq;

// Compute sin and cos directly on float
sin_data[pos * cache_dim + j] = std::sin(angle);
cos_data[pos * cache_dim + j] = std::cos(angle);
sin_data[pos * cache_dim + j] = std::sin(angle) * table_factor;
cos_data[pos * cache_dim + j] = std::cos(angle) * table_factor;
}
}

Expand Down