stuck here
This commit is contained in:
Xuan Son Nguyen
parent
be101fc117
commit
1fa084e733
|
|
@ -10938,7 +10938,7 @@ class LongcatFlashModel(DeepseekV2Model):
|
|||
|
||||
zero_expert_num = self.hparams["zero_expert_num"]
|
||||
zero_expert_type = self.hparams["zero_expert_type"]
|
||||
assert(zero_expert_type == "identity")
|
||||
assert zero_expert_type == "identity", "cpp implementation only supports 'identity' type"
|
||||
self.gguf_writer.add_n_zero_experts(zero_expert_num)
|
||||
|
||||
def modify_tensors(self, data_torch, name, bid):
|
||||
|
|
|
|||
|
|
@ -89,6 +89,7 @@ add_library(llama
|
|||
models/llada.cpp
|
||||
models/llama-iswa.cpp
|
||||
models/llama.cpp
|
||||
models/longcat-flash.cpp
|
||||
models/maincoder.cpp
|
||||
models/mamba.cpp
|
||||
models/mimo2-iswa.cpp
|
||||
|
|
|
|||
|
|
@ -1114,6 +1114,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
|
|||
const int64_t n_tokens = cur->ne[1];
|
||||
const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
|
||||
|
||||
// longcat-flash use n_zero_experts
|
||||
const int64_t n_probs = n_expert + hparams.n_zero_experts;
|
||||
|
||||
ggml_tensor * logits = nullptr;
|
||||
|
||||
if (probs_in == nullptr) {
|
||||
|
|
@ -1169,7 +1172,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
|
|||
// select top n_group_used expert groups
|
||||
// https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/e815299b0bcbac849fa540c768ef21845365c9eb/modeling_deepseek.py#L440-L457
|
||||
if (hparams.n_expert_groups > 1 && n_tokens > 0) {
|
||||
const int64_t n_exp_per_group = n_expert / hparams.n_expert_groups;
|
||||
const int64_t n_exp_per_group = n_probs / hparams.n_expert_groups;
|
||||
|
||||
// organize experts into n_expert_groups
|
||||
ggml_tensor * selection_groups = ggml_reshape_3d(ctx0, selection_probs, n_exp_per_group, hparams.n_expert_groups, n_tokens); // [n_exp_per_group, n_expert_groups, n_tokens]
|
||||
|
|
@ -1187,7 +1190,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
|
|||
// mask out the other groups
|
||||
selection_probs = ggml_get_rows(ctx0, selection_groups, expert_groups); // [n_exp_per_group, n_group_used, n_tokens]
|
||||
selection_probs = ggml_set_rows(ctx0, ggml_fill(ctx0, selection_groups, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
|
||||
selection_probs = ggml_reshape_2d(ctx0, selection_probs, n_expert, n_tokens); // [n_expert, n_tokens]
|
||||
selection_probs = ggml_reshape_2d(ctx0, selection_probs, n_probs, n_tokens); // [n_probs, n_tokens]
|
||||
cb(selection_probs, "ffn_moe_probs_masked", il);
|
||||
}
|
||||
|
||||
|
|
@ -1201,6 +1204,12 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
|
|||
ggml_tensor * f_sel = ggml_cast(ctx0, selected_experts, GGML_TYPE_F32);
|
||||
selected_experts = ggml_cast(ctx0, ggml_scale(ctx0, f_sel, 1.0f / float(hparams.n_group_experts)), GGML_TYPE_I32);
|
||||
probs = ggml_reshape_3d(ctx0, probs, 1, hparams.n_expert, n_tokens);
|
||||
|
||||
} else if (arch == LLM_ARCH_LONGCAT_FLASH && hparams.n_zero_experts > 0) {
|
||||
ggml_tensor * f_sel = ggml_cast(ctx0, selected_experts, GGML_TYPE_F32);
|
||||
// TODO (hard): how to implement zero-computation experts here?
|
||||
probs = ggml_reshape_3d(ctx0, probs, 1, n_probs, n_tokens);
|
||||
|
||||
} else {
|
||||
probs = ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -7032,7 +7032,12 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
// try to see if this is a dense or MoE layer
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert_full}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
if (!layer.ffn_gate_inp) {
|
||||
bool is_moe = (layer.ffn_gate_inp != nullptr);
|
||||
if (is_moe && (i % 2 != 0)) {
|
||||
throw std::runtime_error("MoE layers must be at even indices");
|
||||
}
|
||||
|
||||
if (!is_moe) {
|
||||
// dense
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
|
|
@ -8167,6 +8172,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_mimo2_iswa>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_LONGCAT_FLASH:
|
||||
{
|
||||
llm = std::make_unique<llm_build_longcat_flash>(*this, params);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,210 @@
|
|||
#include "models.h"
|
||||
|
||||
llm_build_longcat_flash::llm_build_longcat_flash(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const bool is_mla = hparams.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();
|
||||
|
||||
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 uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
// large part of the code is copied from deepseek2
|
||||
// we only use a subset of features here
|
||||
// TODO: dedup the code by abstracting common parts
|
||||
GGML_ASSERT(is_mla);
|
||||
GGML_ASSERT(kv_lora_rank > 0);
|
||||
|
||||
// longcat-flash uses double attention + MLP, so n_layer must be even
|
||||
GGML_ASSERT(n_layer % 2 == 0);
|
||||
|
||||
const float kq_scale = 1.0f / 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();
|
||||
|
||||
auto * inp_attn_k = build_attn_inp_k(); // MLA-only
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++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 * q = NULL;
|
||||
|
||||
///////// MLA implementation - exactly the same as deepseek2 /////////
|
||||
|
||||
q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
|
||||
cb(q, "q", il);
|
||||
|
||||
q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(q, "q", il);
|
||||
|
||||
q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
|
||||
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);
|
||||
|
||||
{
|
||||
// {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 optimzation converts into MQA (ie: GQA with 1 group)
|
||||
cur = build_attn(inp_attn_k,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
|
||||
}
|
||||
|
||||
///////// End of MLA implementation /////////
|
||||
}
|
||||
|
||||
if (il == n_layer - 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);
|
||||
|
||||
bool is_moe = model.layers[il].ffn_gate_inp != nullptr;
|
||||
|
||||
if (!is_moe) {
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
model.layers[il].ffn_gate, NULL, NULL,
|
||||
model.layers[il].ffn_down, NULL, NULL,
|
||||
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, hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// FFN shared expert
|
||||
{
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, NULL, NULL,
|
||||
model.layers[il].ffn_gate_shexp, NULL, NULL,
|
||||
model.layers[il].ffn_down_shexp, NULL, NULL,
|
||||
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);
|
||||
}
|
||||
|
|
@ -316,6 +316,10 @@ struct llm_build_llama_iswa : public llm_graph_context {
|
|||
llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_longcat_flash : public llm_graph_context {
|
||||
llm_build_longcat_flash(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_maincoder : public llm_graph_context {
|
||||
llm_build_maincoder(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue