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model : support for DeepseekV32ForCausalLM with generic DeepSeek Sparse Attention (DSA) implementation (#23346)
* llama : support DeepSeek V3.2 model family (with DSA lightning indexer) * convert : handle DeepseekV32ForCausalLM architecture * ggml : support for f16 GGML_OP_FILL * memory : separate hparams argument in llama_kv_cache constructor * memory : add llama_kv_cache_dsa memory (KV cache + lightning indexer cache) * llama : support for LLM_ARCH_DEEPSEEK32 * model : llama_model_deepseek32 implementation * model : merge two scale operations into one in DSA lightning indexer implementation * chore : remove unused code * model : support NVFP4 in DeepSeek V3.2 Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * memory : refactoring TODO Co-authored-by: ggerganov <ggerganov@users.noreply.github.com> --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
This commit is contained in:
fairydreaming
GitHub
parent
031ddb2e08
commit
1f0aa2a696
22 changed files with 1261 additions and 7 deletions
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@ -47,6 +47,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
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"DeepseekForCausalLM": "deepseek",
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"DeepseekV2ForCausalLM": "deepseek",
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"DeepseekV3ForCausalLM": "deepseek",
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"DeepseekV32ForCausalLM": "deepseek",
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"DistilBertForMaskedLM": "bert",
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"DistilBertForSequenceClassification": "bert",
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"DistilBertModel": "bert",
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@ -915,6 +915,8 @@ class ModelBase:
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gguf.MODEL_TENSOR.SSM_CONV1D_Q,
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gguf.MODEL_TENSOR.SSM_CONV1D_K,
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gguf.MODEL_TENSOR.SSM_CONV1D_V,
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# DSA indexer weights should be F32
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gguf.MODEL_TENSOR.INDEXER_PROJ,
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)
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)
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or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
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@ -386,3 +386,32 @@ class DeepseekV2Model(TextModel):
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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@ModelBase.register("DeepseekV32ForCausalLM")
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class DeepseekV32Model(DeepseekV2Model):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK32
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skip_mtp = False
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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def set_vocab(self):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
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assert getattr(tokenizer, "add_bos_token", False), "Change value of add_bos_token to true in tokenizer_config.json file."
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self._set_vocab_gpt2()
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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# NextN/MTP prediction layers
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if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
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self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
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# DSA indexer parameters
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self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
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self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
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self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
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@ -2235,8 +2235,42 @@ static void ggml_compute_forward_fill_f32(const ggml_compute_params * params, gg
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}
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}
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static void ggml_compute_forward_fill_f16(const ggml_compute_params * params, ggml_tensor * dst) {
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const ggml_fp16_t c = GGML_CPU_FP32_TO_FP16(ggml_get_op_params_f32(dst, 0));
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GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
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GGML_TENSOR_LOCALS(size_t, nb, dst, nb);
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const auto [ir0, ir1] = get_thread_range(params, dst);
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for (int64_t ir = ir0; ir < ir1; ++ir) {
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const int64_t i03 = ir/(ne2*ne1);
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const int64_t i02 = (ir - i03*ne2*ne1)/ne1;
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const int64_t i01 = (ir - i03*ne2*ne1 - i02*ne1);
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ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1);
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ggml_vec_set_f16(ne0, dst_ptr, c);
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}
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}
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void ggml_compute_forward_fill(const ggml_compute_params * params, ggml_tensor * dst) {
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ggml_compute_forward_fill_f32(params, dst);
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const ggml_tensor * src0 = dst->src[0];
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_fill_f32(params, dst);
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} break;
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_fill_f16(params, dst);
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} break;
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default:
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{
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GGML_ABORT("unsupported type for ggml_compute_forward_fill: %s", ggml_type_name(src0->type));
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}
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}
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}
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// ggml_compute_tri
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@ -5223,7 +5223,7 @@ static struct ggml_tensor * ggml_fill_impl(
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struct ggml_tensor * a,
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float c,
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bool inplace) {
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GGML_ASSERT(a->type == GGML_TYPE_F32);
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GGML_ASSERT(a->type == GGML_TYPE_F32 || a->type == GGML_TYPE_F16);
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GGML_ASSERT(ggml_is_contiguous(a));
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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@ -451,6 +451,7 @@ class MODEL_ARCH(IntEnum):
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DEEPSEEK = auto()
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DEEPSEEK2 = auto()
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DEEPSEEK2OCR = auto()
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DEEPSEEK32 = auto()
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CHATGLM = auto()
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GLM4 = auto()
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GLM4_MOE = auto()
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@ -967,6 +968,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.DEEPSEEK: "deepseek",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.DEEPSEEK2OCR: "deepseek2-ocr",
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MODEL_ARCH.DEEPSEEK32: "deepseek32",
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.GLM4: "glm4",
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MODEL_ARCH.GLM4_MOE: "glm4moe",
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@ -2930,6 +2932,46 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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],
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MODEL_ARCH.DEEPSEEK32: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_Q_A,
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MODEL_TENSOR.ATTN_Q_B,
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MODEL_TENSOR.ATTN_KV_A_MQA,
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MODEL_TENSOR.ATTN_K_B,
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MODEL_TENSOR.ATTN_V_B,
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MODEL_TENSOR.ATTN_Q_A_NORM,
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MODEL_TENSOR.ATTN_KV_A_NORM,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.FFN_GATE_SHEXP,
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_EXP_PROBS_B,
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MODEL_TENSOR.INDEXER_K_NORM,
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MODEL_TENSOR.INDEXER_PROJ,
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MODEL_TENSOR.INDEXER_ATTN_K,
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MODEL_TENSOR.INDEXER_ATTN_Q_B,
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# NextN/MTP tensors - preserved but unused
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MODEL_TENSOR.NEXTN_EH_PROJ,
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MODEL_TENSOR.NEXTN_EMBED_TOKENS,
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MODEL_TENSOR.NEXTN_ENORM,
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MODEL_TENSOR.NEXTN_HNORM,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
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MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
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],
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MODEL_ARCH.ERNIE4_5_MOE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -4077,6 +4119,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.DEEPSEEK32: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.CHATGLM: [
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MODEL_TENSOR.ROPE_FREQS,
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],
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@ -24,6 +24,7 @@ add_library(llama
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llama-io.cpp
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llama-kv-cache.cpp
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llama-kv-cache-iswa.cpp
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llama-kv-cache-dsa.cpp
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llama-memory.cpp
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llama-memory-hybrid.cpp
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llama-memory-hybrid-iswa.cpp
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@ -75,6 +75,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_DEEPSEEK, "deepseek" },
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_DEEPSEEK2OCR, "deepseek2-ocr" },
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{ LLM_ARCH_DEEPSEEK32, "deepseek32" },
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{ LLM_ARCH_CHATGLM, "chatglm" },
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{ LLM_ARCH_GLM4, "glm4" },
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{ LLM_ARCH_GLM4_MOE, "glm4moe" },
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@ -904,6 +905,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
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case LLM_ARCH_OLMO2:
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case LLM_ARCH_OLMOE:
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case LLM_ARCH_DEEPSEEK2:
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case LLM_ARCH_DEEPSEEK32:
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case LLM_ARCH_GLM_DSA:
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case LLM_ARCH_BITNET:
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case LLM_ARCH_T5:
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@ -79,6 +79,7 @@ enum llm_arch {
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LLM_ARCH_DEEPSEEK,
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_DEEPSEEK2OCR,
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LLM_ARCH_DEEPSEEK32,
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LLM_ARCH_CHATGLM,
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LLM_ARCH_GLM4,
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LLM_ARCH_GLM4_MOE,
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@ -7,6 +7,7 @@
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-iswa.h"
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#include "llama-kv-cache-dsa.h"
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#include "llama-memory-hybrid.h"
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#include "llama-memory-hybrid-iswa.h"
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#include "llama-memory-recurrent.h"
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@ -531,6 +532,34 @@ bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) {
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return res;
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}
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void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
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mctx->get_mla()->set_input_k_idxs(self_k_idxs_mla, ubatch);
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mctx->get_mla()->set_input_kq_mask(self_kq_mask_mla, ubatch, cparams.causal_attn);
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mctx->get_lid()->set_input_k_idxs(self_k_idxs_lid, ubatch);
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mctx->get_lid()->set_input_kq_mask(self_kq_mask_lid, ubatch, cparams.causal_attn);
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mctx->get_lid()->set_input_k_rot(self_k_rot_lid);
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}
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bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
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const auto * mctx = static_cast<const llama_kv_cache_dsa_context *>(params.mctx);
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this->mctx = mctx;
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bool res = true;
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res &= self_k_idxs_mla->ne[0] == params.ubatch.n_tokens;
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res &= self_k_idxs_lid->ne[0] == params.ubatch.n_tokens;
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res &= can_reuse_kq_mask(self_kq_mask_mla, mctx->get_mla(), params.ubatch, params.cparams);
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res &= can_reuse_kq_mask(self_kq_mask_lid, mctx->get_lid(), params.ubatch, params.cparams);
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return res;
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}
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void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
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// base tensors may not be allocated if there are no non-SWA attention layers
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if (self_k_idxs && self_k_idxs->buffer) {
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@ -2396,6 +2425,82 @@ ggml_tensor * llm_graph_context::build_attn(
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return cur;
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}
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ggml_tensor * llm_graph_context::build_attn(
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llm_graph_input_attn_k_dsa * inp,
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ggml_tensor * wo,
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ggml_tensor * wo_b,
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ggml_tensor * wo_s,
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ggml_tensor * q_cur,
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ggml_tensor * k_cur,
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ggml_tensor * v_cur,
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ggml_tensor * kq_b,
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ggml_tensor * sinks,
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ggml_tensor * v_mla,
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ggml_tensor * top_k,
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float kq_scale,
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int il) const {
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// these nodes are added to the graph together so that they are not reordered
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// by doing so, the number of splits in the graph is reduced
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// expand k later to enable rope fusion which directly writes into k-v cache
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ggml_build_forward_expand(gf, q_cur);
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ggml_build_forward_expand(gf, v_cur);
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ggml_build_forward_expand(gf, k_cur);
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const auto * mctx_cur = inp->mctx->get_mla();
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// store to KV cache
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{
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const auto & k_idxs = inp->get_k_idxs_mla();
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ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
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}
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const auto & kq_mask = inp->get_kq_mask_mla();
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// prepare new kq mask - starts filled with -INFINITY
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ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY);
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// reshape KQ mask into tensor with rows of size 1:
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// [n_kv, n_batch, 1, n_stream] -> [1, n_kv, n_batch, n_stream]
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kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3], kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0);
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// reshape top_k indices: [n_top_k, n_batch, 1, n_stream] -> [n_top_k, n_batch, n_stream, 1]
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ggml_tensor * top_k_3d = ggml_view_4d(ctx0, top_k, top_k->ne[0], top_k->ne[1], top_k->ne[3], 1, top_k->nb[1], top_k->nb[2], top_k->ne[3]*top_k->nb[3], 0);
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// prepare zero-filled tensor with rows of size 1: [1, n_top_k, n_batch, n_stream]
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// this will be our source of zero values for unmasking top k mask elements
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ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]);
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zeros = ggml_fill(ctx0, zeros, 0.0f);
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// modify KQ mask by unmasking elements that are in top_k indices
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// ggml_set_rows([1, n_kv, n_batch, n_stream], [1, n_top_k, n_batch, n_stream], [n_top_k, n_batch, n_stream, 1])
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ggml_tensor * kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d);
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// reshape to restore the original shape of KQ mask:
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// [1, n_kv, n_batch, n_stream] -> [n_kv, n_batch, 1, n_stream]
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kq_mask_top_k = ggml_view_4d(ctx0, kq_mask_top_k, kq_mask_top_k->ne[1], kq_mask_top_k->ne[2], 1, kq_mask_top_k->ne[3], kq_mask_top_k->nb[2], kq_mask_top_k->nb[3], kq_mask_top_k->nb[3], 0);
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// combine with the original kq mask
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kq_mask_top_k = ggml_add(ctx0, kq_mask_top_k, kq_mask);
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ggml_tensor * q = q_cur;
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ggml_tensor * k = mctx_cur->get_k(ctx0, il);
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ggml_tensor * v = ggml_view_4d(ctx0, k, v_cur->ne[0], k->ne[1], k->ne[2], k->ne[3], k->nb[1], k->nb[2], k->nb[3], 0);
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ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask_top_k, sinks, v_mla, kq_scale, il);
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cb(cur, "kqv_out", il);
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if (wo) {
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cur = build_lora_mm(wo, cur, wo_s);
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}
|
||||
|
||||
if (wo_b) {
|
||||
cur = ggml_add(ctx0, cur, wo_b);
|
||||
}
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
ggml_tensor * llm_graph_context::build_attn(
|
||||
llm_graph_input_attn_kv_iswa * inp,
|
||||
ggml_tensor * wo,
|
||||
|
|
@ -2542,6 +2647,30 @@ ggml_tensor * llm_graph_context::build_attn(
|
|||
return cur;
|
||||
}
|
||||
|
||||
llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
|
||||
const auto * mctx_cur = static_cast<const llama_kv_cache_dsa_context *>(mctx);
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_attn_k_dsa>(hparams, cparams, mctx_cur);
|
||||
|
||||
{
|
||||
inp->self_k_idxs_mla = mctx_cur->get_mla()->build_input_k_idxs(ctx0, ubatch);
|
||||
|
||||
inp->self_kq_mask_mla = build_attn_inp_kq_mask(ctx0, mctx_cur->get_mla(), ubatch, cparams);
|
||||
inp->self_kq_mask_mla_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_mla, GGML_TYPE_F16) : inp->self_kq_mask_mla;
|
||||
}
|
||||
|
||||
{
|
||||
inp->self_k_idxs_lid = mctx_cur->get_lid()->build_input_k_idxs(ctx0, ubatch);
|
||||
|
||||
inp->self_kq_mask_lid = build_attn_inp_kq_mask(ctx0, mctx_cur->get_lid(), ubatch, cparams);
|
||||
inp->self_kq_mask_lid_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_lid, GGML_TYPE_F16) : inp->self_kq_mask_lid;
|
||||
|
||||
inp->self_k_rot_lid = mctx_cur->get_lid()->build_input_k_rot(ctx0);
|
||||
}
|
||||
|
||||
return (llm_graph_input_attn_k_dsa *) res->add_input(std::move(inp));
|
||||
}
|
||||
|
||||
// TODO: maybe separate the inner implementation into a separate function
|
||||
// like with the non-sliding window equivalent
|
||||
// once sliding-window hybrid caches are a thing.
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ struct llama_layer;
|
|||
struct llama_memory_context_i;
|
||||
|
||||
class llama_kv_cache_context;
|
||||
class llama_kv_cache_dsa_context;
|
||||
class llama_kv_cache_iswa_context;
|
||||
class llama_memory_recurrent_context;
|
||||
class llama_memory_hybrid_context;
|
||||
|
|
@ -373,6 +374,44 @@ public:
|
|||
const llama_kv_cache_context * mctx;
|
||||
};
|
||||
|
||||
class llm_graph_input_attn_k_dsa : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_attn_k_dsa(
|
||||
const llama_hparams & hparams,
|
||||
const llama_cparams & cparams,
|
||||
const llama_kv_cache_dsa_context * mctx) :
|
||||
hparams(hparams),
|
||||
cparams(cparams),
|
||||
mctx(mctx) {
|
||||
}
|
||||
~llm_graph_input_attn_k_dsa() = default;
|
||||
|
||||
void set_input(const llama_ubatch * ubatch) override;
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override;
|
||||
|
||||
ggml_tensor * get_k_idxs_mla() const { return self_k_idxs_mla; }
|
||||
ggml_tensor * get_k_idxs_lid() const { return self_k_idxs_lid; }
|
||||
|
||||
ggml_tensor * get_kq_mask_mla() const { return self_kq_mask_mla_cnv; }
|
||||
ggml_tensor * get_kq_mask_lid() const { return self_kq_mask_lid; }
|
||||
|
||||
ggml_tensor * self_k_idxs_mla = nullptr; // I64 [n_batch]
|
||||
ggml_tensor * self_k_idxs_lid = nullptr; // I64 [n_batch]
|
||||
|
||||
ggml_tensor * self_kq_mask_mla = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
|
||||
ggml_tensor * self_kq_mask_mla_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
|
||||
ggml_tensor * self_kq_mask_lid = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
|
||||
ggml_tensor * self_kq_mask_lid_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
|
||||
|
||||
ggml_tensor * self_k_rot_lid = nullptr;
|
||||
|
||||
const llama_hparams hparams;
|
||||
const llama_cparams cparams;
|
||||
|
||||
const llama_kv_cache_dsa_context * mctx;
|
||||
};
|
||||
|
||||
class llm_graph_input_attn_kv_iswa : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_attn_kv_iswa(
|
||||
|
|
@ -973,6 +1012,23 @@ struct llm_graph_context {
|
|||
float kq_scale,
|
||||
int il) const;
|
||||
|
||||
llm_graph_input_attn_k_dsa * build_attn_inp_k_dsa() const;
|
||||
|
||||
ggml_tensor * build_attn(
|
||||
llm_graph_input_attn_k_dsa * inp,
|
||||
ggml_tensor * wo,
|
||||
ggml_tensor * wo_b,
|
||||
ggml_tensor * wo_s,
|
||||
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
|
||||
ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
|
||||
ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
|
||||
ggml_tensor * kq_b,
|
||||
ggml_tensor * sinks, // [n_head_q]
|
||||
ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
|
||||
ggml_tensor * top_k, // [n_indexer_top_k, n_tokens]
|
||||
float kq_scale,
|
||||
int il) const;
|
||||
|
||||
llm_graph_input_attn_kv_iswa * build_attn_inp_kv_iswa() const;
|
||||
|
||||
// note: if k_cur or v_cur are not provided, they will not be stored in the memory
|
||||
|
|
|
|||
261
src/llama-kv-cache-dsa.cpp
Normal file
261
src/llama-kv-cache-dsa.cpp
Normal file
|
|
@ -0,0 +1,261 @@
|
|||
#include "llama-kv-cache-dsa.h"
|
||||
|
||||
#include "llama-impl.h"
|
||||
#include "llama-batch.h"
|
||||
#include "llama-model.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cassert>
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa
|
||||
//
|
||||
|
||||
llama_kv_cache_dsa::llama_kv_cache_dsa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool unified,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_pad,
|
||||
uint32_t n_swa,
|
||||
llama_swa_type swa_type,
|
||||
const layer_filter_cb & filter,
|
||||
const layer_reuse_cb & reuse) :
|
||||
hparams_lid(model.hparams), n_stream(unified ? 1 : n_seq_max) {
|
||||
|
||||
LLAMA_LOG_INFO("%s: creating main KV cache, size = %u cells\n", __func__, kv_size);
|
||||
|
||||
kv_mla = std::make_unique<llama_kv_cache>(
|
||||
model, model.hparams, type_k, type_v,
|
||||
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
|
||||
n_swa, swa_type, filter, reuse);
|
||||
|
||||
// we use llama_kv_cache for caching indexer keys
|
||||
// by hand-tweaking some hparams we fool it to create
|
||||
// indexer key cache tensors with correct dimensions
|
||||
// https://github.com/ggml-org/llama.cpp/pull/21149#discussion_r3015940823
|
||||
|
||||
// DSA lightning indexer uses MQA with single key head
|
||||
std::fill(hparams_lid.n_head_kv_arr.begin(), hparams_lid.n_head_kv_arr.end(), 1);
|
||||
hparams_lid.n_embd_head_k_full = model.hparams.indexer_head_size;
|
||||
hparams_lid.rope_type = LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
LLAMA_LOG_INFO("%s: creating indexer KV cache, size = %u cells\n", __func__, kv_size);
|
||||
|
||||
kv_lid = std::make_unique<llama_kv_cache>(
|
||||
model, hparams_lid, type_k, type_v,
|
||||
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
|
||||
n_swa, swa_type, filter, reuse);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::clear(bool data) {
|
||||
kv_mla->clear(data);
|
||||
kv_lid->clear(data);
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
||||
bool res = true;
|
||||
|
||||
res = res & kv_mla->seq_rm(seq_id, p0, p1);
|
||||
res = res & kv_lid->seq_rm(seq_id, p0, p1);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
||||
kv_mla->seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
kv_lid->seq_cp(seq_id_src, seq_id_dst, p0, p1);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::seq_keep(llama_seq_id seq_id) {
|
||||
kv_mla->seq_keep(seq_id);
|
||||
kv_lid->seq_keep(seq_id);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
|
||||
kv_mla->seq_add(seq_id, p0, p1, shift);
|
||||
kv_lid->seq_add(seq_id, p0, p1, shift);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
|
||||
kv_mla->seq_div(seq_id, p0, p1, d);
|
||||
kv_lid->seq_div(seq_id, p0, p1, d);
|
||||
}
|
||||
|
||||
llama_pos llama_kv_cache_dsa::seq_pos_min(llama_seq_id seq_id) const {
|
||||
return kv_mla->seq_pos_min(seq_id);
|
||||
}
|
||||
|
||||
llama_pos llama_kv_cache_dsa::seq_pos_max(llama_seq_id seq_id) const {
|
||||
return kv_mla->seq_pos_max(seq_id);
|
||||
}
|
||||
|
||||
std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsa::memory_breakdown() const {
|
||||
std::map<ggml_backend_buffer_type_t, size_t> mb = kv_mla->memory_breakdown();
|
||||
for (const auto & buft_size : kv_lid->memory_breakdown()) {
|
||||
mb[buft_size.first] += buft_size.second;
|
||||
}
|
||||
return mb;
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa::init_batch(
|
||||
llama_batch_allocr & balloc,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_all) {
|
||||
GGML_UNUSED(embd_all);
|
||||
|
||||
do {
|
||||
balloc.split_reset();
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
while (true) {
|
||||
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
|
||||
|
||||
if (ubatch.n_tokens == 0) {
|
||||
break;
|
||||
}
|
||||
|
||||
ubatches.push_back(std::move(ubatch)); // NOLINT
|
||||
}
|
||||
|
||||
if (balloc.get_n_used() < balloc.get_n_tokens()) {
|
||||
// failed to find a suitable split
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_mla = kv_mla->prepare(ubatches);
|
||||
if (sinfos_mla.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
auto sinfos_lid = kv_lid->prepare(ubatches);
|
||||
if (sinfos_lid.empty()) {
|
||||
break;
|
||||
}
|
||||
|
||||
assert(sinfos_mla.size() == sinfos_lid.size());
|
||||
|
||||
return std::make_unique<llama_kv_cache_dsa_context>(
|
||||
this, std::move(sinfos_mla), std::move(sinfos_lid), std::move(ubatches));
|
||||
} while (false);
|
||||
|
||||
return std::make_unique<llama_kv_cache_dsa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa::init_full() {
|
||||
return std::make_unique<llama_kv_cache_dsa_context>(this);
|
||||
}
|
||||
|
||||
llama_memory_context_ptr llama_kv_cache_dsa::init_update(llama_context * lctx, bool optimize) {
|
||||
return std::make_unique<llama_kv_cache_dsa_context>(this, lctx, optimize);
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa::get_can_shift() const {
|
||||
return kv_mla->get_can_shift() &&
|
||||
kv_lid->get_can_shift() &&
|
||||
kv_mla->get_size() == kv_lid->get_size();
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
|
||||
kv_mla->state_write(io, seq_id, flags);
|
||||
kv_lid->state_write(io, seq_id, flags);
|
||||
}
|
||||
|
||||
void llama_kv_cache_dsa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
|
||||
kv_mla->state_read(io, seq_id, flags);
|
||||
kv_lid->state_read(io, seq_id, flags);
|
||||
}
|
||||
|
||||
llama_kv_cache * llama_kv_cache_dsa::get_mla() const {
|
||||
return kv_mla.get();
|
||||
}
|
||||
|
||||
llama_kv_cache * llama_kv_cache_dsa::get_lid() const {
|
||||
return kv_lid.get();
|
||||
}
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_context
|
||||
//
|
||||
|
||||
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(llama_memory_status status) : status(status) {}
|
||||
|
||||
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv) :
|
||||
ctx_mla(kv->get_mla()->init_full()),
|
||||
ctx_lid(kv->get_lid()->init_full()),
|
||||
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv,
|
||||
llama_context * lctx,
|
||||
bool optimize) :
|
||||
ctx_mla(kv->get_mla()->init_update(lctx, optimize)),
|
||||
ctx_lid(kv->get_lid()->init_update(lctx, optimize)),
|
||||
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_context::llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv,
|
||||
slot_info_vec_t sinfos_mla,
|
||||
slot_info_vec_t sinfos_lid,
|
||||
std::vector<llama_ubatch> ubatches) :
|
||||
ubatches(std::move(ubatches)),
|
||||
// note: here we copy the ubatches. not sure if this is ideal
|
||||
ctx_mla(new llama_kv_cache_context(kv->get_mla(), std::move(sinfos_mla), this->ubatches)),
|
||||
ctx_lid(new llama_kv_cache_context(kv->get_lid(), std::move(sinfos_lid), this->ubatches)),
|
||||
status(llama_memory_status_combine(ctx_mla->get_status(), ctx_lid->get_status())) {
|
||||
}
|
||||
|
||||
llama_kv_cache_dsa_context:: ~llama_kv_cache_dsa_context() = default;
|
||||
|
||||
bool llama_kv_cache_dsa_context::next() {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
ctx_mla->next();
|
||||
ctx_lid->next();
|
||||
|
||||
if (++i_next >= ubatches.size()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool llama_kv_cache_dsa_context::apply() {
|
||||
assert(!llama_memory_status_is_fail(status));
|
||||
|
||||
bool res = true;
|
||||
|
||||
res = res & ctx_mla->apply();
|
||||
res = res & ctx_lid->apply();
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
llama_memory_status llama_kv_cache_dsa_context::get_status() const {
|
||||
return status;
|
||||
}
|
||||
|
||||
const llama_ubatch & llama_kv_cache_dsa_context::get_ubatch() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return ubatches[i_next];
|
||||
}
|
||||
|
||||
const llama_kv_cache_context * llama_kv_cache_dsa_context::get_mla() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return static_cast<const llama_kv_cache_context *>(ctx_mla.get());
|
||||
}
|
||||
|
||||
const llama_kv_cache_context * llama_kv_cache_dsa_context::get_lid() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return static_cast<const llama_kv_cache_context *>(ctx_lid.get());
|
||||
}
|
||||
138
src/llama-kv-cache-dsa.h
Normal file
138
src/llama-kv-cache-dsa.h
Normal file
|
|
@ -0,0 +1,138 @@
|
|||
#pragma once
|
||||
|
||||
#include "llama-kv-cache.h"
|
||||
|
||||
#include <vector>
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa
|
||||
//
|
||||
|
||||
// utilizes two instances of llama_kv_cache:
|
||||
// - the first instance is for caching key tensors of the model,
|
||||
// - the second instance is for caching lightning indexer key tensors
|
||||
|
||||
class llama_kv_cache_dsa : public llama_memory_i {
|
||||
public:
|
||||
llama_kv_cache_dsa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool unified,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_pad,
|
||||
uint32_t n_swa,
|
||||
llama_swa_type swa_type,
|
||||
const layer_filter_cb & filter,
|
||||
const layer_reuse_cb & reuse);
|
||||
|
||||
~llama_kv_cache_dsa() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
llama_memory_context_ptr init_batch(
|
||||
llama_batch_allocr & balloc,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_all) override;
|
||||
|
||||
llama_memory_context_ptr init_full() override;
|
||||
|
||||
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
void clear(bool data) override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa specific API
|
||||
//
|
||||
|
||||
llama_kv_cache * get_mla() const;
|
||||
llama_kv_cache * get_lid() const;
|
||||
|
||||
private:
|
||||
// we keep indexer KV cache hparams instance here as llama_kv_cache stores only reference to it
|
||||
llama_hparams hparams_lid;
|
||||
const uint32_t n_stream = 1;
|
||||
|
||||
std::unique_ptr<llama_kv_cache> kv_mla;
|
||||
std::unique_ptr<llama_kv_cache> kv_lid;
|
||||
};
|
||||
|
||||
class llama_kv_cache_dsa_context : public llama_memory_context_i {
|
||||
public:
|
||||
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
|
||||
|
||||
// used for errors
|
||||
llama_kv_cache_dsa_context(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache context
|
||||
llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv);
|
||||
|
||||
// used to create an update context
|
||||
llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv,
|
||||
llama_context * lctx,
|
||||
bool optimize);
|
||||
|
||||
// used to create a batch processing context from a batch
|
||||
llama_kv_cache_dsa_context(
|
||||
llama_kv_cache_dsa * kv,
|
||||
slot_info_vec_t sinfos_base,
|
||||
slot_info_vec_t sinfos_ik,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_dsa_context();
|
||||
|
||||
//
|
||||
// llama_memory_context_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_dsa_context specific API
|
||||
//
|
||||
|
||||
const llama_kv_cache_context * get_mla() const;
|
||||
const llama_kv_cache_context * get_lid() const;
|
||||
|
||||
private:
|
||||
//llama_kv_cache_dsa * kv;
|
||||
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
const llama_memory_context_ptr ctx_mla;
|
||||
const llama_memory_context_ptr ctx_lid;
|
||||
|
||||
const llama_memory_status status;
|
||||
};
|
||||
|
|
@ -60,14 +60,14 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
|
|||
LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
|
||||
|
||||
kv_base = std::make_unique<llama_kv_cache>(
|
||||
model, type_k, type_v,
|
||||
model, hparams, type_k, type_v,
|
||||
v_trans, offload, unified, size_base, n_seq_max, n_pad,
|
||||
0, LLAMA_SWA_TYPE_NONE, filter_base, reuse);
|
||||
|
||||
LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
|
||||
|
||||
kv_swa = std::make_unique<llama_kv_cache>(
|
||||
model, type_k, type_v,
|
||||
model, hparams, type_k, type_v,
|
||||
v_trans, offload, unified, size_swa, n_seq_max, n_pad,
|
||||
hparams.n_swa, hparams.swa_type, filter_swa, reuse);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -79,6 +79,7 @@ static ggml_tensor * ggml_mul_mat_aux(
|
|||
|
||||
llama_kv_cache::llama_kv_cache(
|
||||
const llama_model & model,
|
||||
const llama_hparams & hparams,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
|
|
@ -91,7 +92,7 @@ llama_kv_cache::llama_kv_cache(
|
|||
llama_swa_type swa_type,
|
||||
const layer_filter_cb & filter,
|
||||
const layer_reuse_cb & reuse) :
|
||||
model(model), hparams(model.hparams), v_trans(v_trans),
|
||||
model(model), hparams(hparams), v_trans(v_trans),
|
||||
n_seq_max(n_seq_max), n_stream(unified ? 1 : n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
|
||||
|
||||
GGML_ASSERT(kv_size % n_pad == 0);
|
||||
|
|
@ -253,7 +254,7 @@ llama_kv_cache::llama_kv_cache(
|
|||
// allocate tensors and initialize the buffers to avoid NaNs in the padding
|
||||
for (auto & [buft, ctx] : ctx_map) {
|
||||
ggml_backend_buffer_t buf;
|
||||
if (model.hparams.no_alloc) {
|
||||
if (hparams.no_alloc) {
|
||||
buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
|
||||
for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
|
||||
t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
|
||||
|
|
@ -293,6 +294,11 @@ llama_kv_cache::llama_kv_cache(
|
|||
ggml_is_quantized(type_k) &&
|
||||
hparams.n_embd_head_k() % 64 == 0;
|
||||
|
||||
// always create Hadamard rotation tensors for DeepSeek V3.2 DSA lightning indexer
|
||||
if (model.arch == LLM_ARCH_DEEPSEEK32 && hparams.n_embd_head_k_full == hparams.indexer_head_size) {
|
||||
attn_rot_k = true;
|
||||
}
|
||||
|
||||
attn_rot_v =
|
||||
!attn_rot_disable &&
|
||||
n_embd_head_v_all > 0 &&
|
||||
|
|
|
|||
|
|
@ -93,8 +93,12 @@ public:
|
|||
|
||||
using slot_info_vec_t = std::vector<slot_info>;
|
||||
|
||||
// TODO: refactor the memory instances to not depend on `llama_model`
|
||||
// instead pass all necessary info (e.g. hparams, dev layers, arch, etc.) directly
|
||||
// likely through `struct llama_memory_params`
|
||||
llama_kv_cache(
|
||||
const llama_model & model,
|
||||
const llama_hparams & hparams,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ llama_memory_hybrid::llama_memory_hybrid(
|
|||
hparams(model.hparams),
|
||||
mem_attn(new llama_kv_cache(
|
||||
model,
|
||||
model.hparams,
|
||||
type_k,
|
||||
type_v,
|
||||
v_trans,
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@
|
|||
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cache-iswa.h"
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
#include "llama-memory-hybrid.h"
|
||||
#include "llama-memory-hybrid-iswa.h"
|
||||
#include "llama-memory-recurrent.h"
|
||||
|
|
@ -172,6 +173,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
|
|||
return new llama_model_deepseek2(params);
|
||||
case LLM_ARCH_DEEPSEEK2OCR:
|
||||
return new llama_model_deepseek2ocr(params);
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
return new llama_model_deepseek32(params);
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
return new llama_model_glm_dsa(params);
|
||||
case LLM_ARCH_MISTRAL4:
|
||||
|
|
@ -779,6 +782,7 @@ const char * llm_type_name(llm_type type) {
|
|||
case LLM_TYPE_310B_A15B: return "310B.A15B";
|
||||
case LLM_TYPE_355B_A32B: return "355B.A32B";
|
||||
case LLM_TYPE_397B_A17B: return "397B.A17B";
|
||||
case LLM_TYPE_685B_A37B: return "685B.A37B";
|
||||
case LLM_TYPE_744B_A40B: return "744B.A40B";
|
||||
case LLM_TYPE_E2B: return "E2B";
|
||||
case LLM_TYPE_E4B: return "E4B";
|
||||
|
|
@ -1769,7 +1773,7 @@ void llama_model::print_info() const {
|
|||
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
||||
}
|
||||
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
|
||||
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
|
||||
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
||||
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
|
||||
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
|
||||
|
|
@ -1957,6 +1961,23 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
{
|
||||
res = nullptr;
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
{
|
||||
res = new llama_kv_cache_dsa(
|
||||
*this,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
nullptr,
|
||||
nullptr);
|
||||
} break;
|
||||
// Models that need standard caching should rely on recurrent/hybrid
|
||||
// checks
|
||||
default:
|
||||
|
|
@ -2083,6 +2104,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
|
||||
res = new llama_kv_cache(
|
||||
*this,
|
||||
hparams,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
|
|
@ -2272,6 +2294,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_DEEPSEEK:
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_DEEPSEEK2OCR:
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_PLM:
|
||||
case LLM_ARCH_CHATGLM:
|
||||
case LLM_ARCH_GRANITE:
|
||||
|
|
|
|||
|
|
@ -137,6 +137,7 @@ enum llm_type {
|
|||
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
|
||||
LLM_TYPE_355B_A32B, // GLM-4.5
|
||||
LLM_TYPE_397B_A17B, // Qwen3.5
|
||||
LLM_TYPE_685B_A37B, // DeepSeek V3.2
|
||||
LLM_TYPE_744B_A40B, // GLM-5
|
||||
LLM_TYPE_E2B,
|
||||
LLM_TYPE_E4B,
|
||||
|
|
|
|||
503
src/models/deepseek32.cpp
Normal file
503
src/models/deepseek32.cpp
Normal file
|
|
@ -0,0 +1,503 @@
|
|||
#include "models.h"
|
||||
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cache-dsa.h"
|
||||
|
||||
void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
hparams.f_norm_eps = 1e-6; // eps for layer norm
|
||||
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
|
||||
|
||||
// MoE parameters
|
||||
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
|
||||
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
||||
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
||||
|
||||
// deepseek MLA parameters
|
||||
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_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
||||
|
||||
// DSA parameters
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
|
||||
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
|
||||
|
||||
// Expert gating function
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
|
||||
|
||||
if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
|
||||
// [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
|
||||
// cancel the factor from the convert script
|
||||
hparams.rope_yarn_log_mul /= 0.1f;
|
||||
}
|
||||
|
||||
// NextN/MTP parameters
|
||||
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
||||
GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
|
||||
|
||||
// TODO: when MTP is implemented, this should probably be updated if needed
|
||||
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 62: type = LLM_TYPE_685B_A37B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
const bool is_mla = hparams.is_mla();
|
||||
if (!is_mla) {
|
||||
throw std::runtime_error("DEEPSEEK32 architecture requires MLA");
|
||||
}
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
|
||||
|
||||
const int64_t q_lora_rank = hparams.n_lora_q;
|
||||
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
const int64_t n_expert_shared = hparams.n_expert_shared;
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
// try to load output.weight, if not found, use token_embd (tied embeddings)
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (!output) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
int flags = 0;
|
||||
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
|
||||
// skip all tensors in the NextN layers
|
||||
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
|
||||
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
|
||||
}
|
||||
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
||||
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
|
||||
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
|
||||
|
||||
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
|
||||
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
|
||||
|
||||
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
|
||||
|
||||
// note: only old legacy GGUF files will have the unsplit wkv_b tensor in
|
||||
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
|
||||
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
// DSA indexer
|
||||
layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
|
||||
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
|
||||
layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
|
||||
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
|
||||
if (i < (int) hparams.n_layer_dense_lead) {
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
|
||||
} else {
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
if (n_expert == 0) {
|
||||
throw std::runtime_error("n_expert must be > 0");
|
||||
}
|
||||
if (n_expert_used == 0) {
|
||||
throw std::runtime_error("n_expert_used must be > 0");
|
||||
}
|
||||
|
||||
// MoE branch
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
|
||||
|
||||
// Shared expert branch
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
}
|
||||
|
||||
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
|
||||
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
|
||||
|
||||
// Optional tensors
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
|
||||
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_deepseek32::build_arch_graph(const llm_graph_params & params) const {
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const bool is_mla = hparams.is_mla();
|
||||
GGML_ASSERT(is_mla);
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();
|
||||
GGML_UNUSED(n_embd_head_v);
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
|
||||
|
||||
const int64_t n_indexer_head = hparams.indexer_n_head;
|
||||
const int64_t n_embd_indexer_head = hparams.indexer_head_size;
|
||||
const int64_t n_embd_indexer_head_rope = hparams.n_rot();
|
||||
const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope;
|
||||
const uint32_t n_indexer_top_k = hparams.indexer_top_k;
|
||||
|
||||
const uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
|
||||
// See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.
|
||||
// And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
|
||||
|
||||
// first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
|
||||
GGML_ASSERT(ext_factor >= 0.0f);
|
||||
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
|
||||
|
||||
// use the original attn_factor to pre-scale the kq_scale
|
||||
const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
|
||||
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
// {n_embd, n_tokens}
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa();
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
int effective_n_layers = hparams.n_layer - hparams.nextn_predict_layers;
|
||||
for (int il = 0; il < effective_n_layers; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self_attention
|
||||
{
|
||||
ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
|
||||
cb(qr, "qr", il);
|
||||
|
||||
qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(qr, "qr", il);
|
||||
|
||||
ggml_tensor * top_k = nullptr;
|
||||
|
||||
// lightning indexer
|
||||
{
|
||||
ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
|
||||
// split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens}
|
||||
ggml_tensor * indexer_q_pe =
|
||||
ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens,
|
||||
ggml_row_size(indexer_q->type, n_embd_indexer_head),
|
||||
ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0);
|
||||
cb(indexer_q_pe, "indexer_q_pe", il);
|
||||
|
||||
// and {n_embd_indexer_head_nope, n_indexer_head, n_tokens}
|
||||
ggml_tensor * indexer_q_nope =
|
||||
ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens,
|
||||
ggml_row_size(indexer_q->type, n_embd_indexer_head),
|
||||
ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head,
|
||||
ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
|
||||
cb(indexer_q_nope, "indexer_q_nope", il);
|
||||
|
||||
indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot,
|
||||
LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(indexer_q_pe, "indexer_q_pe", il);
|
||||
|
||||
// {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens}
|
||||
indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
|
||||
ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// split into {n_embd_indexer_head_rope, 1, n_tokens}
|
||||
ggml_tensor * indexer_k_pe =
|
||||
ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens,
|
||||
ggml_row_size(indexer_k->type, n_embd_indexer_head),
|
||||
ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0);
|
||||
cb(indexer_k_pe, "indexer_k_pe", il);
|
||||
|
||||
// and {n_embd_indexer_head_nope, 1, n_tokens}
|
||||
ggml_tensor * indexer_k_nope =
|
||||
ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens,
|
||||
ggml_row_size(indexer_k->type, n_embd_indexer_head),
|
||||
ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1,
|
||||
ggml_row_size(indexer_k->type, n_embd_indexer_head_nope));
|
||||
cb(indexer_k_nope, "indexer_k_nope", il);
|
||||
|
||||
indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot,
|
||||
LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(indexer_k_pe, "indexer_k_pe", il);
|
||||
|
||||
// {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens}
|
||||
indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// perform Hadamard transform on indexer q and k
|
||||
indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
indexer_k = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_k);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
// store indexer keys to KV cache
|
||||
const auto * mctx_lid = inp_attn_dsa->mctx->get_lid();
|
||||
const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid();
|
||||
ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
|
||||
|
||||
// prepare indexer weights
|
||||
ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);
|
||||
cb(indexer_weights, "indexer_weights", il);
|
||||
|
||||
// get cached indexer keys
|
||||
indexer_k = mctx_lid->get_k(ctx0, il);
|
||||
|
||||
// split the batch into streams if needed
|
||||
const auto n_stream = indexer_k->ne[3];
|
||||
indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
|
||||
indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
|
||||
|
||||
// calculate indexer kq
|
||||
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
|
||||
cb(indexer_q, "indexer_q", il);
|
||||
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
|
||||
cb(indexer_k, "indexer_k", il);
|
||||
|
||||
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
|
||||
cb(indexer_kq, "indexer_kq", il);
|
||||
|
||||
// ReLU requires contiguous tensors
|
||||
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
|
||||
cb(indexer_kq, "indexer_kq", il);
|
||||
|
||||
// apply ReLU
|
||||
ggml_tensor * indexer_score = ggml_relu(ctx0, indexer_kq);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// pre-scale weights to avoid scaling operations on huge indexer_score tensor
|
||||
indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
|
||||
cb(indexer_weights, "indexer_weights", il);
|
||||
|
||||
// multiply scores by indexer weights
|
||||
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// sum by q n_indexer_head dimension
|
||||
indexer_score = ggml_sum_rows(ctx0, indexer_score);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// permute result to match KQ mask
|
||||
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// mask indexer scores
|
||||
ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid();
|
||||
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
|
||||
cb(indexer_score, "indexer_score", il);
|
||||
|
||||
// get indices of top k indexer scores
|
||||
uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
|
||||
top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
|
||||
cb(top_k, "top_k", il);
|
||||
}
|
||||
|
||||
ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);
|
||||
cb(q, "q", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head, n_tokens}
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
|
||||
cb(q_nope, "q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "kv_cmpr_pe", il);
|
||||
|
||||
// split into {kv_lora_rank, n_tokens}
|
||||
ggml_tensor * kv_cmpr =
|
||||
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, 1, n_tokens}
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(k_pe, "k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "kv_cmpr", il);
|
||||
|
||||
// MLA attention
|
||||
{
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
|
||||
// note: rope must go first for in-place context shifting in build_rope_shift()
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
|
||||
cur = build_attn(inp_attn_dsa,
|
||||
model.layers[il].wo, NULL, model.layers[il].wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
|
||||
}
|
||||
}
|
||||
if (il == effective_n_layers - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
if ((uint32_t) il < hparams.n_layer_dense_lead) {
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
|
||||
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
|
||||
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
// MoE branch
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
model.layers[il].ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr,
|
||||
model.layers[il].ffn_gate_up_exps,
|
||||
model.layers[il].ffn_up_exps_s,
|
||||
model.layers[il].ffn_gate_exps_s,
|
||||
model.layers[il].ffn_down_exps_s);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// FFN shared expert
|
||||
{
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
|
||||
model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
|
||||
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
}
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
|
@ -1030,6 +1030,19 @@ struct llama_model_deepseek2 : public llama_model_base {
|
|||
};
|
||||
|
||||
|
||||
struct llama_model_deepseek32 : public llama_model_base {
|
||||
llama_model_deepseek32(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
void load_arch_tensors(llama_model_loader & ml) override;
|
||||
|
||||
struct graph : public llm_graph_context {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
|
||||
struct llama_model_deepseek2ocr : public llama_model_base {
|
||||
llama_model_deepseek2ocr(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
|
|
|
|||
|
|
@ -100,6 +100,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
|
|||
n_ff = 96;
|
||||
n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
|
||||
} else if (arch == LLM_ARCH_DEEPSEEK2
|
||||
|| arch == LLM_ARCH_DEEPSEEK32
|
||||
|| arch == LLM_ARCH_GLM_DSA
|
||||
|| arch == LLM_ARCH_KIMI_LINEAR
|
||||
|| arch == LLM_ARCH_MISTRAL4) {
|
||||
|
|
@ -156,6 +157,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
|
|||
|
||||
ms.add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, 8.0f);
|
||||
if (arch == LLM_ARCH_DEEPSEEK2
|
||||
|| arch == LLM_ARCH_DEEPSEEK32
|
||||
|| arch == LLM_ARCH_GLM_DSA
|
||||
|| arch == LLM_ARCH_KIMI_LINEAR
|
||||
|| arch == LLM_ARCH_MISTRAL4) {
|
||||
|
|
@ -332,6 +334,7 @@ static bool moe_mandatory(const llm_arch arch) {
|
|||
case LLM_ARCH_ARCTIC:
|
||||
case LLM_ARCH_DEEPSEEK:
|
||||
case LLM_ARCH_DEEPSEEK2:
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_GLM4_MOE:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
case LLM_ARCH_EXAONE_MOE:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue