ikawrakow/ik_llama.cpp
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Prepare wk_b tensors of DeepSeek models on the fly (#259)

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* FlashMLA-2: eliminate intermediate f32 tensors

This works on the CPU. PP performance is ~13% better for 16k tokens
and compute buffer is quite a bit smaller.

* FlashMLA-2: enable fast path only on the CPU for now

I did implement the necessary ops on CUDA, but something is
still wrong there, so for now we only use it when running
CPU-only.

* FlashMLA-2: slightly smaller computer buffer size

* Prepare wk_b when loading DeepSeek models (if wk_b is missing)

* Add some comments

* Fix case where wkv_b is quantized with k- or i-quants.

* Fix CUDA

There is an issue with quantized GEMV on CUDA when the left operand
(the matrix) is not contiguous. So, for now, we also create wv_b
during model loading and use that instead of the 3D view of wkv_b.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-03-17 09:31:56 +01:00
committed by GitHub
parent 676f0e71b4
commit 0f19a500a9
1 changed files with 152 additions and 21 deletions
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173
src/llama.cpp
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@@ -2640,6 +2640,9 @@ struct llama_layer {
struct ggml_tensor * ffn_gate_scale;
struct ggml_tensor * ffn_up_scale;
struct ggml_tensor * ffn_down_scale;
std::unique_ptr<ggml_tensor> computed_wk_b;
std::unique_ptr<ggml_tensor> computed_wv_b;
};
struct llama_kv_cell {
@@ -3186,17 +3189,6 @@ static bool llama_kv_cache_init(
ggml_tensor * k;
ggml_tensor * v;
if (cparams.mla_attn) {
if (!model.layers[i].wk_b || !model.layers[i].wv_b) {
if (warn) {
LLAMA_LOG_WARN("=======================================================================================\n");
LLAMA_LOG_WARN("%s: missing MLA tensors => disabling MLA\n", __func__);
LLAMA_LOG_WARN("%s: you need to reconvert your model in order to use MLA\n", __func__);
LLAMA_LOG_WARN("=======================================================================================\n");
warn = false;
}
}
}
if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) {
// DeepSeek MLA
const uint32_t n_embd_head_qk_rope = hparams.n_rot;
const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
@@ -8130,6 +8122,139 @@ static bool llm_load_tensors(
}
}
if (model.arch == LLM_ARCH_DEEPSEEK2) {
int n_to_compute = 0;
for (auto& l : model.layers) {
if (!l.wk_b) ++n_to_compute;
}
if (n_to_compute > 0) {
// Prepare wk_b tensors to enable MLA usage also for model files that do not include
// the wk_b tensors (because, e.g., they were converted using mainline llama.cpp)
// We do it here because otherwise wkv_b may get run-time-repacked, which will make
// preparation of wk_b impossible. It also has the benefit that wk_b will get automatically
// run-time repacked if the rtr option is set. The downside is that we will prepare wk_b
// even if it is not needed (because MLA is not being used). If we wanted to avoid
// computing wk_b from wkv_b if not needed, we would need to propagate the context parameters
// to the model loading function. On the other hand, in some hypothetical bright future,
// where we are able to use the optimum settings for the computation, which for DeepSeekV3/R1/Lite
// is no MLA + FA for prompt processing, and MLA + FA for token generation, it would be useful
// to change the MLA setting on the fly, depending on context. In that case, having prepared
// the MLA tensors here is the right ting to do^TM.
const uint32_t n_embd_head_qk_rope = hparams.n_rot;
const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
const int32_t n_embd_head_v = hparams.n_embd_head_v;
const int32_t n_head = hparams.n_head(0);
std::vector<uint8_t> work_data;
LLAMA_LOG_INFO("============ %s: need to compute %d wk_b tensors\n", __func__, n_to_compute);
for (int il = 1; il < n_layer; ++il) {
// Somehow the number of heads is being defined as being per layer. Not sure why this is the
// case, but for now we do not support strange models that have different numbers of heads
// in different model layers.
if (hparams.n_head(il) != n_head) throw std::runtime_error("Unsupported configuration");
}
auto total_size_wkb = 0;
size_t max_wkv_size = 0;
size_t max_wk_size = 0;
for (auto& l : model.layers) {
if (!l.wk_b) {
auto new_type = ggml_is_quantized(l.wkv_b->type) ? GGML_TYPE_Q8_0 : l.wkv_b->type;
auto size = ggml_row_size(new_type, n_embd_head_qk_nope)*kv_lora_rank*n_head;
max_wk_size = std::max(max_wk_size, size);
if (!ggml_backend_buffer_is_host(l.wkv_b->buffer)) {
max_wkv_size = std::max(max_wkv_size, ggml_nbytes(l.wkv_b));
}
}
}
auto context_size = max_wk_size + 2*n_embd_head_qk_nope*kv_lora_rank*n_head*sizeof(float);
context_size *= 2; // just in case;
std::vector<uint8_t> wkv_buffer;
if (max_wkv_size > 0) wkv_buffer.resize(max_wkv_size);
// So, transposing tensors and then making them contiguous as needed for wk_b may or may not
// be supported on all backends. Hence, to be sure that the preparation of wk_b will
// work correctly, we do it on the CPU backend. We then copy the resulting tensor data to
// the bacikend where wkv_b is stored.
ggml_init_params params{context_size, nullptr, true};
auto ctx = ggml_init(params);
auto graph = ggml_new_graph_custom(ctx, 8, false);
std::vector<uint8_t> tensor_data(2*n_embd_head_qk_nope*kv_lora_rank*n_head*sizeof(float) + max_wk_size);
for (int il = 0; il < n_layer; ++il) {
auto& l = model.layers[il];
if (l.wk_b) continue;
auto wkv_b = *l.wkv_b;
if (!ggml_backend_buffer_is_host(l.wkv_b->buffer)) {
ggml_backend_tensor_get(l.wkv_b, wkv_buffer.data(), 0, ggml_nbytes(l.wkv_b));
wkv_b.data = wkv_buffer.data();
}
auto wk_b_view = ggml_view_3d(ctx, &wkv_b, kv_lora_rank, n_embd_head_qk_nope, n_head,
l.wkv_b->nb[1], l.wkv_b->nb[1]*(n_embd_head_qk_nope + n_embd_head_v), 0);
auto wk_b_f32 = ggml_cast(ctx, wk_b_view, GGML_TYPE_F32);
wk_b_f32->data = tensor_data.data();
auto wk_b_f32_tview = ggml_transpose(ctx, wk_b_f32);
auto wk_b_f32_t = ggml_cont(ctx, wk_b_f32_tview);
wk_b_f32_t->data = (char *)wk_b_f32->data + ggml_nbytes(wk_b_f32);
auto new_type = ggml_is_quantized(wkv_b.type) ? GGML_TYPE_Q8_0 : wkv_b.type;
auto wk_b = ggml_cast(ctx, wk_b_f32_t, new_type);
wk_b->data = (char *)wk_b_f32_t->data + ggml_nbytes(wk_b_f32_t);
ggml_build_forward_expand(graph, wk_b);
auto plan = ggml_graph_plan(graph, std::thread::hardware_concurrency()/2);
if (plan.work_size > work_data.size()) work_data.resize(plan.work_size);
plan.work_data = work_data.data();
auto status = ggml_graph_compute(graph, &plan);
if (status != GGML_STATUS_SUCCESS) throw std::runtime_error("Failed to compute wk_b");
auto name = std::string{"blk."} + std::to_string(il) + ".attn_k_b.weight";
l.computed_wk_b = std::make_unique<ggml_tensor>(*wk_b);
l.computed_wk_b->buffer = ggml_backend_buft_alloc_buffer(ggml_backend_buffer_get_type(l.wkv_b->buffer), ggml_nbytes(wk_b));
l.computed_wk_b->data = ggml_backend_buffer_get_base(l.computed_wk_b->buffer);
l.computed_wk_b->op = GGML_OP_NONE; // we absolutely need to do this, else the backend will attempt to find the parents
// of wk_b, which no longer exist, and will therefore crash.
for (int j = 0; j < GGML_MAX_SRC; ++j) l.computed_wk_b->src[j] = nullptr;
ggml_set_name(l.computed_wk_b.get(), name.c_str());
ggml_backend_buffer_set_usage(l.computed_wk_b->buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
ggml_backend_tensor_set(l.computed_wk_b.get(), wk_b->data, 0, ggml_nbytes(wk_b));
l.wk_b = l.computed_wk_b.get();
ggml_graph_clear(graph);
auto wv_b = ggml_cont(ctx, ggml_view_3d(ctx, &wkv_b, kv_lora_rank, n_embd_head_v, n_head,
l.wkv_b->nb[1], l.wkv_b->nb[1]*(n_embd_head_qk_nope + n_embd_head_v), l.wkv_b->nb[1]*n_embd_head_qk_nope));
wv_b->data = tensor_data.data();
ggml_build_forward_expand(graph, wv_b);
plan = ggml_graph_plan(graph, std::thread::hardware_concurrency()/2);
if (plan.work_size > work_data.size()) work_data.resize(plan.work_size);
plan.work_data = work_data.data();
status = ggml_graph_compute(graph, &plan);
if (status != GGML_STATUS_SUCCESS) throw std::runtime_error("Failed to compute wv_b");
name = std::string{"blk."} + std::to_string(il) + ".attn_v_b.weight";
l.computed_wv_b = std::make_unique<ggml_tensor>(*wv_b);
l.computed_wv_b->buffer = ggml_backend_buft_alloc_buffer(ggml_backend_buffer_get_type(l.wkv_b->buffer), ggml_nbytes(wv_b));
l.computed_wv_b->data = ggml_backend_buffer_get_base(l.computed_wv_b->buffer);
l.computed_wv_b->op = GGML_OP_NONE; // we absolutely need to do this, else the backend will attempt to find the parents
// of wk_b, which no longer exist, and will therefore crash.
for (int j = 0; j < GGML_MAX_SRC; ++j) l.computed_wv_b->src[j] = nullptr;
ggml_set_name(l.computed_wv_b.get(), name.c_str());
ggml_backend_buffer_set_usage(l.computed_wv_b->buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
ggml_backend_tensor_set(l.computed_wv_b.get(), wv_b->data, 0, ggml_nbytes(wv_b));
l.wv_b = l.computed_wv_b.get();
printf("Computed %s as %ld x %ld x %ld and stored in buffer %s\n", name.c_str(), wk_b->ne[0], wk_b->ne[1], wk_b->ne[2],
ggml_backend_buffer_name(l.computed_wk_b->buffer));
ggml_graph_clear(graph);
}
ggml_free(ctx);
}
}
if (use_mmap_buffer) {
for (auto & mapping : ml.mappings) {
model.mappings.emplace_back(std::move(mapping));
@@ -13595,7 +13720,7 @@ struct llm_build_context {
LLM_NORM_RMS, cb, il);
cb(kv_compressed, "kv_compressed", il);
if (lctx.cparams.mla_attn && model.layers[il].wk_b && model.layers[il].wv_b) {
if (lctx.cparams.mla_attn) {
ggml_tensor * kv_cache_trans;
@@ -13738,10 +13863,9 @@ struct llm_build_context {
ggml_tensor * kqv_compressed;
struct ggml_tensor * wk_b = ggml_view_3d(ctx0, model.layers[il].wk_b, n_embd_head_qk_nope, kv_lora_rank, n_head,
ggml_row_size(model.layers[il].wk_b->type, n_embd_head_qk_nope),
ggml_row_size(model.layers[il].wk_b->type, kv_lora_rank)*n_embd_head_qk_nope, 0);
cb(wk_b, "wk_b", il);
auto wkv_b = model.layers[il].wkv_b;
auto wk_b = model.layers[il].wk_b->ne[1] == kv_lora_rank ? model.layers[il].wk_b
: ggml_reshape_3d(ctx0, model.layers[il].wk_b, n_embd_head_qk_nope, kv_lora_rank, n_head);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "q_nope_perm", il);
@@ -13832,11 +13956,18 @@ struct llm_build_context {
}
}
struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head,
ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank),
ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank)*n_embd_head_v, 0);
cb(wv_b, "wv_b", il);
std::memcpy(wv_b->name, model.layers[il].wv_b->name, GGML_MAX_NAME);
auto wv_b = model.layers[il].wv_b;
if (wv_b->ne[1] != n_embd_head_v) {
wv_b = ggml_reshape_3d(ctx0, wv_b, kv_lora_rank, n_embd_head_v, n_head);
cb(wv_b, "wv_b", il);
}
// There is an issue with quantized GEMV on CUDA when the left operand (the matrix) is
// not contiguous. So, for now, we create wv_b during model loading and use that
// instead of the commented out 3D view below.
//auto wv_b = ggml_view_3d(ctx0, wkv_b, kv_lora_rank, n_embd_head_v, n_head,
// wkv_b->nb[1], wkv_b->nb[1]*(n_embd_head_v + n_embd_head_qk_nope),
// wkv_b->nb[1]*n_embd_head_qk_nope);
//cb(wv_b, "wv_b", il);
kqv = ggml_mul_mat(ctx0, wv_b, kqv_compressed);
cb(kqv, "kqv", il);