vbatts/llama.cpp
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Added five new DeepSeek-V2-specific parameters:

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- leading_dense_block_count => hparams.n_leading_dense_layer,
- expert_feed_forward_length => hparams.n_expert_ff,
- expert_shared_count => hparams.n_expert_shared,
- attention.q_lora_rank => hparams.n_lora_q,
- attention.kv_lora_rank => hparams.n_lora_kv
This commit is contained in:
Stanisław Szymczyk 2024-05-18 14:40:09 +02:00
parent 039896407a
commit b50c07c247
4 changed files with 66 additions and 20 deletions
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13
convert-hf-to-gguf.py
View file

@ -2401,7 +2401,16 @@ class DeepseekV2Model(Model):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(hparams["v_head_dim"])
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
@ -2410,10 +2419,6 @@ class DeepseekV2Model(Model):
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(hparams["v_head_dim"])
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

5
gguf-py/gguf/constants.py
View file

@ -37,11 +37,14 @@ class Keys:
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
POOLING_TYPE = "{arch}.pooling_type"
LOGIT_SCALE = "{arch}.logit_scale"
@ -55,6 +58,8 @@ class Keys:
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
CAUSAL = "{arch}.attention.causal"
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"

15
gguf-py/gguf/gguf_writer.py
View file

@ -376,9 +376,15 @@ class GGUFWriter:
def add_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
def add_leading_dense_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool) -> None:
self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
@ -409,6 +415,9 @@ class GGUFWriter:
def add_expert_used_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_expert_shared_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count)
def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
@ -418,6 +427,12 @@ class GGUFWriter:
def add_causal_attention(self, value: bool) -> None:
self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
def add_q_lora_rank(self, length: int) -> None:
self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length)
def add_kv_lora_rank(self, length: int) -> None:
self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length)
def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)

53
llama.cpp
View file

@ -288,11 +288,14 @@ enum llm_kv {
LLM_KV_CONTEXT_LENGTH,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_BLOCK_COUNT,
LLM_KV_LEADING_DENSE_BLOCK_COUNT,
LLM_KV_FEED_FORWARD_LENGTH,
LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
LLM_KV_USE_PARALLEL_RESIDUAL,
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_EXPERT_SHARED_COUNT,
LLM_KV_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
@ -305,6 +308,8 @@ enum llm_kv {
LLM_KV_ATTENTION_LAYERNORM_EPS,
LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
LLM_KV_ATTENTION_CAUSAL,
LLM_KV_ATTENTION_Q_LORA_RANK,
LLM_KV_ATTENTION_KV_LORA_RANK,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@ -365,11 +370,14 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
{ LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" },
{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
{ LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" },
{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
@ -382,6 +390,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
{ LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@ -1803,6 +1813,12 @@ struct llama_hparams {
uint32_t n_expert_used = 0;
uint32_t n_vocab_type = 0; // for BERT-style token types
uint32_t n_leading_dense_layer = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
uint32_t n_expert_ff = 0;
uint32_t n_expert_shared = 0;
float f_norm_eps;
float f_norm_rms_eps;
@ -1842,6 +1858,12 @@ struct llama_hparams {
if (this->n_expert != other.n_expert) return true;
if (this->n_expert_used != other.n_expert_used) return true;
if (this->n_leading_dense_layer != other.n_leading_dense_layer) return true;
if (this->n_lora_q != other.n_lora_q) return true;
if (this->n_lora_kv != other.n_lora_kv) return true;
if (this->n_expert_ff != other.n_expert_ff) return true;
if (this->n_expert_shared != other.n_expert_shared) return true;
if (this->rope_finetuned != other.rope_finetuned) return true;
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
@ -4306,6 +4328,12 @@ static void llm_load_hparams(
case LLM_ARCH_DEEPSEEK2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_leading_dense_layer);
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_expert_ff);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
model.type = e_model::MODEL_UNKNOWN;
} break;
default: (void)0;
@ -6107,16 +6135,12 @@ static bool llm_load_tensors(
} break;
case LLM_ARCH_DEEPSEEK2:
{
// TODO maybe move some of these to hparams
const uint32_t n_shared_experts = 2;
const uint32_t moe_intermediate_size = 1536;
const uint32_t q_lora_rank = 1536;
const uint32_t kv_lora_rank = 512;
const uint32_t first_k_dense_replace = 1;
// kept original names of these parameters from HF transformers code for clarity
const uint32_t qk_rope_head_dim = hparams.n_rot;
const uint32_t qk_nope_head_dim = hparams.n_embd_head_k - hparams.n_rot;
const uint32_t q_lora_rank = hparams.n_lora_q;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
const uint32_t moe_intermediate_size = hparams.n_expert_ff;
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@ -6144,7 +6168,7 @@ static bool llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
if ((uint32_t) i < first_k_dense_replace) {
if ((uint32_t) i < hparams.n_leading_dense_layer) {
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
@ -6160,9 +6184,9 @@ static bool llm_load_tensors(
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, moe_intermediate_size, n_expert});
// Shared expert branch
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, moe_intermediate_size * n_shared_experts});
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { moe_intermediate_size * n_shared_experts, n_embd});
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, moe_intermediate_size * n_shared_experts});
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, moe_intermediate_size * hparams.n_expert_shared});
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { moe_intermediate_size * hparams.n_expert_shared, n_embd});
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, moe_intermediate_size * hparams.n_expert_shared});
}
}
} break;
@ -10893,13 +10917,10 @@ struct llm_build_context {
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
// TODO maybe move some of these to hparams
const uint32_t first_k_dense_replace = 1;
const uint32_t kv_lora_rank = 512;
// kept original names of these parameters from HF transformers code for clarity
const uint32_t qk_rope_head_dim = hparams.n_rot;
const uint32_t qk_nope_head_dim = hparams.n_embd_head_k - hparams.n_rot;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -11022,7 +11043,7 @@ struct llm_build_context {
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
if ((uint32_t) il < first_k_dense_replace) {
if ((uint32_t) il < hparams.n_leading_dense_layer) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);