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[CANN]Rope repeat Optimization #15501

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Aug 25, 2025
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[CANN]Rope repeat Optimization #15501

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201 changes: 120 additions & 81 deletions ggml/src/ggml-cann/aclnn_ops.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1257,12 +1257,20 @@ static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) {

void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_dst) {
GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst);
if(acl_dst == nullptr) {
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCos, acl_src);
} else {
GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst);
}
}

void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_dst) {
GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst);
if(acl_dst == nullptr) {
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSin, acl_src);
} else {
GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst);
}
}

void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx,
Expand Down Expand Up @@ -2221,13 +2229,54 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
ggml_cann_release_resources(ctx, acl_index, acl_value);
}

/**
* @brief Initializes and caches sine/cosine positional encoding values
* (used in RoPE, Rotary Position Embedding) for attention layers.
*
* This function computes and caches the sin/cos values of
* θ = position * theta_scale for RoPE encoding. The cache is shared
* across attention layers, and only the first attention layer will
* trigger initialization. The cache includes repeated sin/cos values
* with different repeat methods depending on the @param is_neox flag.
*
* Steps performed by this function:
* 1. Identify whether the target tensor belongs to Q/K in attention
* and restrict computation to the first layer only.
* 2. Initialize the theta scale array (arange → power → freq scaling).
* 3. Allocate sin/cos caches if the max prompt length increases.
* 4. Compute θ = position * theta_scale.
* 5. Compute sin(θ), cos(θ) and optionally scale by attn_factor.
* 6. Expand sin/cos values by repeat or repeat_interleave depending
* on whether @param is_neox is enabled.
* 7. Store the computed values into persistent buffers
* (ctx.rope_sin_ptr / ctx.rope_cos_ptr).
*
* @param ctx The CANN backend context, holding memory pool,
* stream, and persistent buffers for rope init/cache.
* @param dst The destination ggml_tensor whose computation
* depends on the cached RoPE values (usually Qcur/Kcur).
* @param theta_scale Scalar exponent base for computing theta scale values.
* @param freq_scale Frequency scaling factor, applied to theta scale.
* @param attn_factor Attention scaling factor, applied to sin/cos.
* @param is_neox Whether to use Neox-style repeat strategy
* (dim expansion vs repeat_interleave).
*/
static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
aclTensor* acl_cos_repeat_tensor,
aclTensor* acl_sin_repeat_tensor,
float theta_scale, float freq_scale,
float attn_factor, bool is_neox) {
// int sin/cos cache, cache has different repeat method depond on
// @param.is_neox
bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);

// used for accuracy testing
bool is_attention = is_q || is_k;

// just compute in first layer in attention
bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
if(is_attention && !is_fisrt_layer) {
return;
}

ggml_tensor* src0 = dst->src[0]; // input
ggml_tensor* src1 = dst->src[1]; // position
Expand All @@ -2253,21 +2302,16 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
}

bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);

// used for accuracy testing
bool is_attention = is_q || is_k;

if(ctx.init_ptr == nullptr || !is_attention) {
// init theta scale, just one time
if(ctx.rope_init_ptr == nullptr || !is_attention) {
// theta_scale arange, [0,1,...,ne00/2 - 1]
if(ctx.init_ptr != nullptr){
ACL_CHECK(aclrtFree(ctx.init_ptr));
if(ctx.rope_init_ptr != nullptr){
ACL_CHECK(aclrtFree(ctx.rope_init_ptr));
}
ACL_CHECK(aclrtMalloc(&ctx.init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
ACL_CHECK(aclrtMalloc(&ctx.rope_init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));

aclTensor* acl_theta_scale_tensor =
ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
float start = 0;
float step = 1;
Expand Down Expand Up @@ -2297,74 +2341,75 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
ggml_cann_release_resources(ctx, acl_theta_scale_tensor,acl_theta_scale);
}

if(ctx.sin_ptr == nullptr) {
int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
}
// init sin_repeat && cos_repeat, one token just init in 0 layer
if(position_length > ctx.max_prompt_length) {
ctx.max_prompt_length = position_length;
int64_t theta_length = theta_scale_length * ctx.max_prompt_length;
ACL_CHECK(aclrtFree(ctx.sin_ptr));
ACL_CHECK(aclrtFree(ctx.cos_ptr));
ACL_CHECK(aclrtMalloc(&ctx.sin_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
ACL_CHECK(aclrtMalloc(&ctx.cos_ptr, theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
int64_t repeat_theta_length = theta_scale_length * ctx.max_prompt_length * 2;
if(ctx.rope_sin_ptr != nullptr) {
ACL_CHECK(aclrtFree(ctx.rope_sin_ptr));
ACL_CHECK(aclrtFree(ctx.rope_cos_ptr));
}
ACL_CHECK(aclrtMalloc(&ctx.rope_sin_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
ACL_CHECK(aclrtMalloc(&ctx.rope_cos_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
}

bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);

if(is_fisrt_layer || !is_attention) {

aclTensor* acl_theta_scale_tensor =
ggml_cann_create_tensor(ctx.init_ptr, ACL_FLOAT, sizeof(float_t),
aclTensor* acl_theta_scale_tensor =
ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);

// position
aclTensor* acl_position_tensor = ggml_cann_create_tensor(
src1->data, ggml_cann_type_mapping(src1->type),
ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);

// power * position
int64_t theta_length = theta_scale_length * position_length;
ggml_cann_pool_alloc theta_allocator(ctx.pool(),
theta_length * sizeof(float_t));
void* theta_buffer = theta_allocator.get();

aclTensor* acl_theta_tensor =
ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
theta_ne, theta_nb, GGML_MAX_DIMS);
aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
acl_theta_tensor);

// sin/cos
aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);

aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);

// release
ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
acl_theta_tensor, acl_sin_tensor, acl_cos_tensor);
}

// position
aclTensor* acl_position_tensor = ggml_cann_create_tensor(
src1->data, ggml_cann_type_mapping(src1->type),
ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);

// power * position
int64_t theta_length = theta_scale_length * position_length;
ggml_cann_pool_alloc theta_allocator(ctx.pool(),
theta_length * sizeof(float_t));
void* theta_buffer = theta_allocator.get();

aclTensor* acl_theta_tensor =
ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
theta_ne, theta_nb, GGML_MAX_DIMS);
aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
acl_theta_tensor);

// sin/cos
ggml_cann_pool_alloc sin_allocator(ctx.pool(),
theta_length * sizeof(float_t));
void* sin_buffer = sin_allocator.get();
aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
ctx.sin_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);

ggml_cann_pool_alloc cos_allocator(ctx.pool(),
theta_length * sizeof(float_t));
void* cos_buffer = cos_allocator.get();
aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
ctx.cos_ptr, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
GGML_MAX_DIMS, ACL_FORMAT_ND);
aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);

// attn_factor
if (attn_factor != 1) {
aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true);
aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true);
}

int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
size_t sin_reshape_nb[GGML_MAX_DIMS];
sin_reshape_nb[0] = sizeof(float_t);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
}
aclTensor* acl_sin_repeat_tensor =
ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
aclTensor* acl_cos_repeat_tensor =
ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);

// repeat
if (is_neox) {
int64_t repeatsArray[] = {1, 1, 1, 2};
Expand All @@ -2380,8 +2425,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
num_repeats, output_size);
}

// release
ggml_cann_release_resources(ctx, acl_sin_tensor, acl_cos_tensor);
ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
acl_theta_tensor, acl_sin_tensor, acl_sin_repeat_tensor, acl_cos_tensor,
acl_cos_repeat_tensor);
}

#ifdef __cplusplus
Expand Down Expand Up @@ -2435,13 +2481,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {

const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;

// init cos/sin cache
ggml_cann_pool_alloc sin_allocator(
ctx.pool(), ne00 * ne02 * sizeof(float_t));
ggml_cann_pool_alloc cos_allocator(
ctx.pool(), ne00 * ne02 * sizeof(float_t));
void* sin_buffer = sin_allocator.get();
void* cos_buffer = cos_allocator.get();
// init ctx.rope_cos/rope_sin cache
aclnn_cache_init(ctx, dst, theta_scale, freq_scale, attn_factor, is_neox);

int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
size_t sin_reshape_nb[GGML_MAX_DIMS];
Expand All @@ -2450,13 +2491,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
}
aclTensor* acl_sin_reshape_tensor =
ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t),
ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
aclTensor* acl_cos_reshape_tensor =
ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t),
ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor,
theta_scale, freq_scale, attn_factor, is_neox);

aclTensor* acl_src = ggml_cann_create_tensor(src0);
aclTensor* acl_dst = ggml_cann_create_tensor(dst);
Expand Down
28 changes: 18 additions & 10 deletions ggml/src/ggml-cann/common.h
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Original file line number Diff line number Diff line change
Expand Up @@ -368,17 +368,19 @@ struct ggml_backend_cann_context {
std::string name; /**< Name of the device. */
std::string description; /**< Description of the device. */
aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
void* init_ptr = nullptr;
void* sin_ptr = nullptr;
void* cos_ptr = nullptr;
int64_t max_prompt_length = 65536;
#ifdef USE_ACL_GRAPH
/// Cached CANN ACL graph used for executing the current ggml computation graph.
std::unique_ptr<ggml_cann_graph> cann_graph;
#endif
cann_task_queue task_queue;
bool async_mode;
bool support_set_rows;
// Rope Cache
void* rope_init_ptr = nullptr;
void* rope_sin_ptr = nullptr;
void* rope_cos_ptr = nullptr;
int64_t max_prompt_length = 0;
// Constant Pool
void* f32_zero_cache = nullptr;
void* f32_one_cache = nullptr;
int64_t f32_zero_cache_element = 0;
Expand Down Expand Up @@ -422,14 +424,20 @@ struct ggml_backend_cann_context {
ACL_CHECK(aclrtDestroyStream(streams[i]));
}
}
if(init_ptr != nullptr) {
ACL_CHECK(aclrtFree(init_ptr));
if(rope_init_ptr != nullptr) {
ACL_CHECK(aclrtFree(rope_init_ptr));
}
if(sin_ptr != nullptr) {
ACL_CHECK(aclrtFree(sin_ptr));
if(rope_sin_ptr != nullptr) {
ACL_CHECK(aclrtFree(rope_sin_ptr));
}
if(cos_ptr != nullptr) {
ACL_CHECK(aclrtFree(cos_ptr));
if(rope_cos_ptr != nullptr) {
ACL_CHECK(aclrtFree(rope_cos_ptr));
}
if(f32_zero_cache != nullptr) {
ACL_CHECK(aclrtFree(f32_zero_cache));
}
if(f32_one_cache != nullptr) {
ACL_CHECK(aclrtFree(f32_one_cache));
}
}

Expand Down
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