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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2026-01-05 20:57:51 +08:00
commit 79f0948344
14 changed files with 150 additions and 77 deletions
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14
convert_hf_to_gguf.py
View file

@ -771,9 +771,14 @@ class TextModel(ModelBase):
self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
rope_theta = self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)
local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "swa_rope_theta", "rope_local_base_freq"], optional=True)
# Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None:
if local_rope_theta is not None:
self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
if "rope_theta" not in self.rope_parameters and rope_theta is not None:
self.rope_parameters["rope_theta"] = rope_theta
if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
self.rope_parameters["rope_type"] = rope_type
@ -839,6 +844,7 @@ class TextModel(ModelBase):
self.gguf_writer.add_head_count_kv(n_head_kv)
logger.info(f"gguf: key-value head count = {n_head_kv}")
# TODO: Handle "sliding_attention" similarly when models start implementing it
rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
if (rope_type := rope_params.get("rope_type")) is not None:
rope_factor = rope_params.get("factor")
@ -885,6 +891,9 @@ class TextModel(ModelBase):
if (rope_theta := rope_params.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
logger.info(f"gguf: rope theta = {rope_theta}")
if (local_rope_theta := self.rope_parameters.get("sliding_attention", {}).get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base_swa(local_rope_theta)
logger.info(f"gguf: rope theta swa = {local_rope_theta}")
if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
@ -5004,7 +5013,6 @@ class Plamo3Model(TextModel):
if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
self.gguf_writer.add_sliding_window(sliding_window)
self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])
self.gguf_writer.add_rope_freq_base_swa(self.rope_parameters.get("sliding_attention", {"rope_theta": self.hparams.get("rope_local_theta")})["rope_theta"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
@ -7480,7 +7488,6 @@ class MimoV2Model(TextModel):
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
self.gguf_writer.add_sliding_window_pattern(self.hparams["hybrid_layer_pattern"])
self.gguf_writer.add_rope_freq_base_swa(self.hparams["swa_rope_theta"])
self.gguf_writer.add_value_length(self.hparams["v_head_dim"])
self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
@ -10218,7 +10225,6 @@ class ModernBertModel(BertModel):
self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
if (sliding_window_pattern := self.hparams.get("global_attn_every_n_layers")) is not None:
self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
self.gguf_writer.add_rope_freq_base_swa(self.rope_parameters.get("sliding_attention", {"rope_theta": self.hparams.get("local_rope_theta")})["rope_theta"])
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])

57
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -2914,39 +2914,41 @@ static void ggml_vk_load_shaders(vk_device& device) {
const uint32_t tk_m = device->coopmat_support ? device->coopmat_k : 1;
const uint32_t tk_s = device->coopmat_support ? device->coopmat_k : 1;
l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };
const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32;
l_warptile_mmq = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
m_warptile_mmq = { 128, 64, 64, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
s_warptile_mmq = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };
l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
s_warptile = { subgroup_size_32, 32, 32, 16, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };
l_warptile_mmq = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
m_warptile_mmq = { 128, 64, 64, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
s_warptile_mmq = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };
// Integer MMQ has a smaller shared memory profile, but heavier register use
l_warptile_mmq_int = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 };
m_warptile_mmq_int = { 128, 64, 64, 32, subgroup_size_8, 32, 2, 2, 2, 1, subgroup_size_8 };
s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, 32, 32, 2, 2, 1, 1, subgroup_size_8 };
l_warptile_mmq_int = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 };
m_warptile_mmq_int = { 128, 64, 64, 32, subgroup_size_8, 32, 2, 2, 2, 1, subgroup_size_8 };
s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, 2, 1, 1, subgroup_size_8 };
// K-quants use even more registers, mitigate by setting WMITER to 1
l_warptile_mmq_int_k = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 1, 4, 4, 1, subgroup_size_8 };
m_warptile_mmq_int_k = { 128, 64, 64, 32, subgroup_size_8, 32, 1, 2, 2, 1, subgroup_size_8 };
s_warptile_mmq_int_k = { subgroup_size_32, 32, 32, 32, 32, 32, 1, 2, 1, 1, subgroup_size_8 };
l_warptile_mmq_int_k = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 1, 4, 4, 1, subgroup_size_8 };
m_warptile_mmq_int_k = { 128, 64, 64, 32, subgroup_size_8, 32, 1, 2, 2, 1, subgroup_size_8 };
s_warptile_mmq_int_k = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 1, 2, 1, 1, subgroup_size_8 };
l_warptile_id = { 128, 128, 128, 16, mul_mat_subgroup_size_16 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_16 };
m_warptile_id = { 128, 64, 64, 16, mul_mat_subgroup_size_16, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_16 };
s_warptile_id = { mul_mat_subgroup_size_16, 32, 32, 16, 32, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_16 };
l_warptile_id = { 128, 128, 128, 16, mul_mat_subgroup_size_16 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_16 };
m_warptile_id = { 128, 64, 64, 16, mul_mat_subgroup_size_16, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_16 };
s_warptile_id = { mul_mat_subgroup_size_16, 32, 32, 16, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_16 };
l_warptile_mmqid = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_8 };
m_warptile_mmqid = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_8 };
s_warptile_mmqid = { mul_mat_subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_8 };
l_warptile_mmqid = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_8 };
m_warptile_mmqid = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_8 };
s_warptile_mmqid = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_8 };
l_warptile_mmqid_int = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, 4, 4, 1, mul_mat_subgroup_size_8 };
m_warptile_mmqid_int = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, 2, 2, 1, mul_mat_subgroup_size_8 };
s_warptile_mmqid_int = { mul_mat_subgroup_size_32, 32, 32, 32, 32, 32, 2, 2, 1, 1, mul_mat_subgroup_size_8 };
l_warptile_mmqid_int = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, 4, 4, 1, mul_mat_subgroup_size_8 };
m_warptile_mmqid_int = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, 2, 2, 1, mul_mat_subgroup_size_8 };
s_warptile_mmqid_int = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, 2, 1, 1, mul_mat_subgroup_size_8 };
l_warptile_mmqid_int_k = { 128, 128, 128, 32, mul_mat_subgroup_size_16 * 2, 64, 1, 4, 4, 1, mul_mat_subgroup_size_16 };
m_warptile_mmqid_int_k = { 128, 64, 64, 32, mul_mat_subgroup_size_16, 32, 1, 2, 2, 1, mul_mat_subgroup_size_16 };
s_warptile_mmqid_int_k = { mul_mat_subgroup_size_32, 32, 32, 32, 32, 32, 1, 2, 1, 1, mul_mat_subgroup_size_16 };
l_warptile_mmqid_int_k = { 128, 128, 128, 32, mul_mat_subgroup_size_16 * 2, 64, 1, 4, 4, 1, mul_mat_subgroup_size_16 };
m_warptile_mmqid_int_k = { 128, 64, 64, 32, mul_mat_subgroup_size_16, 32, 1, 2, 2, 1, mul_mat_subgroup_size_16 };
s_warptile_mmqid_int_k = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 1, 2, 1, 1, mul_mat_subgroup_size_16 };
// chip specific tuning
if ((device->architecture == AMD_GCN) && (device->driver_id != vk::DriverId::eAmdProprietary)) {
@ -6803,7 +6805,12 @@ static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& sub
vk_pipeline pipeline = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, std::array<uint32_t, 1>{ne}, { ne, 1, 1 });
const uint32_t num_blocks = CEIL_DIV(ne, pipeline->wg_denoms[0]);
// clamp the number of elements to the max workgroup count. The shader will iterate over the total number of blocks.
const uint64_t max_elements = std::min<uint64_t>(uint64_t{ctx->device->properties.limits.maxComputeWorkGroupCount[0]} * pipeline->wg_denoms[0], std::numeric_limits<uint32_t>::max());
const uint32_t elements = std::min(ne, static_cast<uint32_t>(max_elements));
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, std::array<uint32_t, 2>{ ne, num_blocks }, { elements, 1, 1 });
ggml_vk_sync_buffers(ctx, subctx);
}

16
ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp
View file

@ -15,6 +15,7 @@
layout (push_constant) uniform parameter
{
uint ne;
uint num_blocks;
} p;
#include "types.glsl"
@ -33,8 +34,7 @@ layout (binding = 1) writeonly buffer D {block_q8_1_x4 data_b[];};
shared float shmem[GROUP_SIZE];
#endif
void quantize() {
const uint wgid = gl_WorkGroupID.x;
void quantize(const uint wgid) {
const uint tid = INVOCATION_ID;
// Each thread handles a vec4, so 8 threads handle a block
@ -45,11 +45,7 @@ void quantize() {
const uint ib = wgid * blocks_per_group + block_in_wg;
const uint iqs = tid % 8;
#ifndef QBLOCK_X4
if (ib >= gl_NumWorkGroups.x * blocks_per_group) {
return;
}
#else
#ifdef QBLOCK_X4
const uint ibx4_outer = ib / 4;
const uint ibx4_inner = ib % 4;
@ -123,5 +119,9 @@ void quantize() {
}
void main() {
quantize();
uint wgid = gl_WorkGroupID.x;
while (wgid < p.num_blocks) {
quantize(wgid);
wgid += gl_NumWorkGroups.x;
}
}

19
ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
View file

@ -101,6 +101,10 @@ void main() {
const uint lane = gl_SubgroupInvocationID;
float probs[experts_per_thread];
[[unroll]]
for (int i = 0; i < experts_per_thread; i++) {
probs[i] = -INFINITY;
}
[[unroll]]
for (uint i = 0; i < n_experts; i += WARP_SIZE) {
@ -112,8 +116,9 @@ void main() {
softmax_warp_inplace(probs, n_experts, lane, nexperts_use_push);
} else if (gating_func == GATING_FUNC_SIGMOID) {
[[unroll]]
for (int i = 0; i < experts_per_thread; i++) {
probs[i] = 1.f / (1.f + exp(-probs[i]));
for (uint i = 0; i < n_experts; i += WARP_SIZE) {
const uint expert = i + lane;
probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? 1.f / (1.f + exp(-probs[i / WARP_SIZE])) : -INFINITY;
}
}
@ -150,11 +155,11 @@ void main() {
uint max_expert = lane;
[[unroll]]
for (int i = 1; i < experts_per_thread; i++) {
const uint expert = lane + i * WARP_SIZE;
if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && selection_probs[i] > max_val_s) {
max_val = probs[i];
max_val_s = selection_probs[i];
for (uint i = WARP_SIZE; i < n_experts; i += WARP_SIZE) {
const uint expert = i + lane;
if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && selection_probs[i / WARP_SIZE] > max_val_s) {
max_val = probs[i / WARP_SIZE];
max_val_s = selection_probs[i / WARP_SIZE];
max_expert = expert;
}
}

4
src/llama-graph.cpp
View file

@ -1326,6 +1326,10 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
res->add_input(std::move(inp));
// make sure the produced embeddings are immediately materialized in the ggml graph
// ref: https://github.com/ggml-org/llama.cpp/pull/18599
ggml_build_forward_expand(gf, cur);
return cur;
}

4
src/llama-hparams.h
View file

@ -105,9 +105,9 @@ struct llama_hparams {
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_base_train_swa;
float rope_freq_base_train_swa = 10000.0f;
float rope_freq_scale_train;
float rope_freq_scale_train_swa;
float rope_freq_scale_train_swa = 1.0f;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul = 0.0f;

49
src/llama-model.cpp
View file

@ -687,6 +687,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
// TODO: Handle SWA metadata similarly when models start implementing it
// rope_freq_scale (inverse of the kv) is optional
float ropescale = 0.0f;
if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
@ -695,10 +696,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
// by default assume that the sliding-window layers use the same scaling type as the non-sliding-window layers
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
// non-transformer models do not have attention heads
@ -786,6 +783,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.f_attn_temp_scale = 0.1f;
hparams.f_attn_temp_offset = 1.0f;
hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
}
switch (hparams.n_expert) {
@ -831,6 +832,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
if (hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(4);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
}
@ -1352,7 +1357,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
if (found_swa && hparams.n_swa > 0) {
uint32_t swa_period = 8;
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
hparams.set_swa_pattern(swa_period);
@ -1418,7 +1422,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.n_swa = 4096; // default value of gemma 2
hparams.set_swa_pattern(2);
hparams.attn_soft_cap = true;
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
@ -1443,8 +1450,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(6);
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
}
@ -1474,10 +1480,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.set_swa_pattern(5);
hparams.n_layer_kv_from_start = 20;
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
hparams.f_attention_scale = 1.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -1493,9 +1498,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.set_swa_pattern(6);
hparams.causal_attn = false; // embeddings do not use causal attention
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
@ -1634,7 +1638,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
{
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(4);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -1673,6 +1680,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
if (found_swa && hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(4);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = 1.0; // See olmo2.cpp
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
}
@ -2015,6 +2026,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.n_swa = 4096;
hparams.set_swa_pattern(4);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
}
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
@ -2317,6 +2332,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(2);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
switch (hparams.n_layer) {
case 24: type = LLM_TYPE_20B; break;
case 36: type = LLM_TYPE_120B; break;
@ -2361,6 +2380,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.n_swa = 4096;
hparams.set_swa_pattern(4, true);
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
hparams.n_no_rope_layer_step = hparams.n_layer;
@ -7261,6 +7284,10 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
LLAMA_LOG_INFO("%s: freq_base_swa = %.1f\n", __func__, hparams.rope_freq_base_train_swa);
LLAMA_LOG_INFO("%s: freq_scale_swa = %g\n", __func__, hparams.rope_freq_scale_train_swa);
}
LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
LLAMA_LOG_INFO("%s: rope_yarn_log_mul= %.4f\n", __func__, hparams.rope_yarn_log_mul);
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");

14
src/models/afmoe.cpp
View file

@ -22,8 +22,15 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
for (int il = 0; il < n_layer; ++il) {
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
ggml_tensor * inpSA = inpL;
// This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous
const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
(il + 1) % hparams.n_no_rope_layer_step != 0;
// dual attention normalization (pre)
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
@ -56,19 +63,16 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
cb(Qcur, "Qcur_normed", il);
cb(Kcur, "Kcur_normed", il);
// RoPE only for sliding_attention layers
const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
((il + 1) % hparams.n_no_rope_layer_step) != 0;
if (use_rope) {
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur_rope", il);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur_rope", il);
}

3
src/models/cohere2-iswa.cpp
View file

@ -21,6 +21,9 @@ llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const
for (int il = 0; il < n_layer; ++il) {
const bool is_swa = hparams.is_swa(il);
// UNUSED:
// const float freq_base_l = model.get_rope_freq_base (cparams, il);
// const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);

7
src/models/gemma2-iswa.cpp
View file

@ -19,6 +19,9 @@ llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const ll
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
@ -43,12 +46,12 @@ llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const ll
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur", il);

8
src/models/llama-iswa.cpp
View file

@ -25,8 +25,12 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
ggml_tensor * inpSA = inpL;
// This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous
const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
(il + 1) % hparams.n_no_rope_layer_step != 0;
@ -67,13 +71,13 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
if (use_rope) {
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);
} else if (inp_attn_scale) {

7
src/models/modern-bert.cpp
View file

@ -23,7 +23,8 @@ llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const ll
auto * inp_attn = build_attn_inp_no_cache();
for (int il = 0; il < n_layer; ++il) {
float freq_base_l = model.get_rope_freq_base(cparams, il);
const float freq_base_l = model.get_rope_freq_base(cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
cur = inpL;
@ -48,13 +49,13 @@ llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const ll
// RoPE
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);

7
src/models/openai-moe-iswa.cpp
View file

@ -14,6 +14,9 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
ggml_tensor * inpSA = inpL;
// norm
@ -49,13 +52,13 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow
);

18
src/models/smallthinker.cpp
View file

@ -26,10 +26,16 @@ llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model,
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
ggml_tensor * probs = nullptr;
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL); // [n_expert, n_tokens]
ggml_tensor * inpSA = inpL;
// This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous
const bool use_rope = hparams.n_no_rope_layer_step == n_layer ||
il % hparams.n_no_rope_layer_step != 0;
ggml_tensor * probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL); // [n_expert, n_tokens]
cb(probs, "ffn_moe_logits", il);
// norm
@ -52,11 +58,11 @@ llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model,
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
if (use_rope) {
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);