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llama : make all KQ masks f16 if FA is used, remove zero attention bias, remove raw_k repeats in DeepSeek V4 (#25370)

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* llama : make all KQ masks (except the lightning indexer one) f16 if FA is used and remove zero attention bias in DeepSeek V4

* llama : remove dead code that repeats unified raw_k cache for each stream in DeepSeek V4 - no longer needed as raw_k is always non-unified.

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
This commit is contained in:
fairydreaming
2026-07-10 09:06:58 +02:00
committed by GitHub
parent 082b326fc7
commit 2ed3c1abbb
2 changed files with 30 additions and 64 deletions
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src/llama-graph.cpp
+26 -12
View File
@@ -646,7 +646,7 @@ static void dsv4_set_kq_mask(
return;
}
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(n_stream > 0);
GGML_ASSERT(n_tokens%n_stream == 0);
GGML_ASSERT(dst->ne[0] == plan.n_kv);
@@ -656,13 +656,27 @@ static void dsv4_set_kq_mask(
GGML_ASSERT((int64_t) plan.n_visible.size() == (int64_t) n_tokens);
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
float * data = (float *) dst->data;
if (dst->type == GGML_TYPE_F32) {
float * data = (float *) dst->data;
for (int64_t i = 0; i < (int64_t) n_tokens; ++i) {
const int32_t n_visible = plan.n_visible[i];
for (int64_t i = 0; i < (int64_t) n_tokens; ++i) {
const int32_t n_visible = plan.n_visible[i];
for (int64_t j = 0; j < dst->ne[0]; ++j) {
data[i*dst->ne[0] + j] = j < n_visible ? 0.0f : -INFINITY;
for (int64_t j = 0; j < dst->ne[0]; ++j) {
data[i*dst->ne[0] + j] = j < n_visible ? 0.0f : -INFINITY;
}
}
} else if (dst->type == GGML_TYPE_F16) {
ggml_fp16_t * data = (ggml_fp16_t *) dst->data;
const ggml_fp16_t fp16_ninf = llama_cast<ggml_fp16_t>(-INFINITY);
const ggml_fp16_t fp16_zero = llama_cast<ggml_fp16_t>(0.0f);
for (int64_t i = 0; i < (int64_t) n_tokens; ++i) {
const int32_t n_visible = plan.n_visible[i];
for (int64_t j = 0; j < dst->ne[0]; ++j) {
data[i*dst->ne[0] + j] = j < n_visible ? fp16_zero : fp16_ninf;
}
}
}
}
@@ -679,8 +693,7 @@ static ggml_tensor * dsv4_build_raw_kq_mask(
GGML_ASSERT(n_stream > 0);
GGML_ASSERT(n_tokens%n_stream == 0);
const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || n_stream == 1);
const auto type = use_fattn ? GGML_TYPE_F16 : GGML_TYPE_F32;
const auto type = cparams.flash_attn ? GGML_TYPE_F16 : GGML_TYPE_F32;
ggml_tensor * res = ggml_new_tensor_4d(ctx, type, n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(res);
@@ -814,6 +827,7 @@ static void dsv4_build_comp_inputs(
llm_graph_input_dsv4::comp_input & inp,
const llama_kv_cache_dsv4_context::comp_plan & plan,
const char * name,
const llama_cparams & cparams,
int64_t n_stream) {
inp.state_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_pos.size(), std::string("dsv4_") + name + "_state_pos");
inp.state_persist_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_src_idxs.size(), std::string("dsv4_") + name + "_state_persist_src_idxs");
@@ -828,7 +842,7 @@ static void dsv4_build_comp_inputs(
GGML_ASSERT(n_stream > 0);
GGML_ASSERT(n_tokens%n_stream == 0);
inp.kq_mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, plan.n_kv, n_tokens/n_stream, 1, n_stream);
inp.kq_mask = ggml_new_tensor_4d(ctx, cparams.flash_attn && strcmp(name, "lid") != 0 ? GGML_TYPE_F16 : GGML_TYPE_F32, plan.n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(inp.kq_mask);
ggml_set_name(inp.kq_mask, (std::string("dsv4_") + name + "_kq_mask").c_str());
}
@@ -3076,9 +3090,9 @@ llm_graph_input_dsv4 * llm_graph_context::build_inp_dsv4() const {
inp_raw->self_k_rot = raw_ctx->build_input_k_rot(ctx0);
auto inp = std::make_unique<llm_graph_input_dsv4>(cparams, std::move(inp_raw), mctx_cur);
dsv4_build_comp_inputs(ctx0, inp->inp_csa, mctx_cur->get_csa_plan(ubatch), "csa", n_stream);
dsv4_build_comp_inputs(ctx0, inp->inp_hca, mctx_cur->get_hca_plan(ubatch), "hca", n_stream);
dsv4_build_comp_inputs(ctx0, inp->inp_lid, mctx_cur->get_lid_plan(ubatch), "lid", n_stream);
dsv4_build_comp_inputs(ctx0, inp->inp_csa, mctx_cur->get_csa_plan(ubatch), "csa", cparams, n_stream);
dsv4_build_comp_inputs(ctx0, inp->inp_hca, mctx_cur->get_hca_plan(ubatch), "hca", cparams, n_stream);
dsv4_build_comp_inputs(ctx0, inp->inp_lid, mctx_cur->get_lid_plan(ubatch), "lid", cparams, n_stream);
inp->inp_csa.k_rot = mctx_cur->get_csa()->build_input_k_rot(ctx0);
inp->inp_hca.k_rot = mctx_cur->get_hca()->build_input_k_rot(ctx0);
inp->inp_lid.k_rot = mctx_cur->get_lid()->build_input_k_rot(ctx0);
src/models/deepseek4.cpp
+4 -52
View File
@@ -184,32 +184,6 @@ static ggml_tensor * dsv4_with_zero_dep(ggml_context * ctx, ggml_tensor * t, ggm
return ggml_add(ctx, t, zero);
}
// Raw SWA K is stored once, but compressed K/masks can carry a stream axis.
// Repeat raw K at graph build time before concatenating raw and compressed K.
static ggml_tensor * dsv4_repeat_streams(ggml_context * ctx, ggml_tensor * t, int64_t n_stream) {
if (t->ne[3] == n_stream) {
return t;
}
GGML_ASSERT(t->ne[3] == 1);
return ggml_repeat_4d(ctx, t, t->ne[0], t->ne[1], t->ne[2], n_stream);
}
static ggml_tensor * dsv4_build_kq_zero_bias(
ggml_context * ctx,
const llama_cparams & cparams,
ggml_tensor * kq_mask,
int64_t n_head) {
if (!cparams.kv_unified || !cparams.flash_attn || kq_mask->ne[3] == 1) {
return nullptr;
}
// Keep multi-stream unified DSV4 on the explicit attention path.
ggml_tensor * res = ggml_new_tensor_4d(ctx, GGML_TYPE_F32,
kq_mask->ne[0], kq_mask->ne[1], n_head, kq_mask->ne[3]);
return ggml_fill(ctx, res, 0.0f);
}
static constexpr int64_t DSV4_CSA_RATIO = 4;
static constexpr int64_t DSV4_HCA_RATIO = 128;
@@ -624,7 +598,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_top_k_mask(
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);
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]);
ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, cparams.flash_attn ? GGML_TYPE_F16 : GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]);
zeros = ggml_fill(ctx0, zeros, 0.0f);
ggml_tensor * kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d);
@@ -681,26 +655,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_csa_lid_attention(
csa_k->nb[1], csa_k->nb[2], csa_k->nb[3], 0);
cb(csa_k, "csa_comp_k", il);
raw_k = dsv4_repeat_streams(ctx0, raw_k, csa_k->ne[3]);
ggml_tensor * k_all = ggml_concat(ctx0, raw_k, csa_k, 2);
cb(k_all, "csa_k_all", il);
ggml_tensor * raw_mask = inp_attn->get_kq_mask();
ggml_tensor * csa_mask = build_top_k_mask(inp_csa.kq_mask, top_k, "csa_top_k_mask", il);
const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || csa_mask->ne[3] == 1);
if (use_fattn && csa_mask->type != GGML_TYPE_F16) {
csa_mask = ggml_cast(ctx0, csa_mask, GGML_TYPE_F16);
}
if (raw_mask->type != csa_mask->type) {
raw_mask = ggml_cast(ctx0, raw_mask, csa_mask->type);
}
ggml_tensor * kq_mask = ggml_concat(ctx0, raw_mask, csa_mask, 0);
cb(kq_mask, "csa_lid_kq_mask", il);
ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
ggml_tensor * out = build_attn_mha(q, k_all, k_all, nullptr, kq_mask, sinks, nullptr, kq_scale, il);
if (k_rot) {
out = llama_mul_mat_hadamard(ctx0, out, k_rot);
}
@@ -746,26 +710,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_hca_attention(
hca_k->nb[1], hca_k->nb[2], hca_k->nb[3], 0);
cb(hca_k, "hca_comp_k", il);
raw_k = dsv4_repeat_streams(ctx0, raw_k, hca_k->ne[3]);
ggml_tensor * k_all = ggml_concat(ctx0, raw_k, hca_k, 2);
cb(k_all, "hca_k_all", il);
ggml_tensor * raw_mask = inp_attn->get_kq_mask();
ggml_tensor * hca_mask = inp_hca.kq_mask;
const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || hca_mask->ne[3] == 1);
if (use_fattn && hca_mask->type != GGML_TYPE_F16) {
hca_mask = ggml_cast(ctx0, hca_mask, GGML_TYPE_F16);
}
if (raw_mask->type != hca_mask->type) {
raw_mask = ggml_cast(ctx0, raw_mask, hca_mask->type);
}
ggml_tensor * kq_mask = ggml_concat(ctx0, raw_mask, hca_mask, 0);
cb(kq_mask, "hca_kq_mask", il);
ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
ggml_tensor * out = build_attn_mha(q, k_all, k_all, nullptr, kq_mask, sinks, nullptr, kq_scale, il);
if (k_rot) {
out = llama_mul_mat_hadamard(ctx0, out, k_rot);
}
@@ -800,10 +754,8 @@ ggml_tensor * llama_model_deepseek4::graph::build_raw_attention(
ggml_tensor * kq_mask = inp_attn->get_kq_mask();
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
k = dsv4_repeat_streams(ctx0, k, kq_mask->ne[3]);
ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
ggml_tensor * out = build_attn_mha(q, k, k, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
ggml_tensor * out = build_attn_mha(q, k, k, nullptr, kq_mask, sinks, nullptr, kq_scale, il);
if (k_rot) {
out = llama_mul_mat_hadamard(ctx0, out, k_rot);
}