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Merged
merged 4 commits into from
Mar 31, 2025
Merged

Sync master with upstream release b4992 #36

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d07a0d7
CANN : remove clang-format in ggml-cann (#12607)
hipudding Mar 29, 2025
a69f846
cmake : fix ccache conflict (#12522)
BusyJay Mar 29, 2025
0bb2919
llama : change cpu_buft_list order: ACCEL -> GPU host -> CPU extra ->…
Djip007 Mar 29, 2025
af6ae1e
llama : fix non-causal mask for gemma 3 (#12615)
ngxson Mar 29, 2025
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2 changes: 1 addition & 1 deletion ggml/src/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ if (GGML_LTO)
endif()
endif()

if (GGML_CCACHE)
if (GGML_CCACHE AND NOT CMAKE_C_COMPILER_LAUNCHER AND NOT CMAKE_CXX_COMPILER_LAUNCHER)
find_program(GGML_CCACHE_FOUND ccache)
find_program(GGML_SCCACHE_FOUND sccache)

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168 changes: 0 additions & 168 deletions ggml/src/ggml-cann/.clang-format
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4 changes: 2 additions & 2 deletions src/llama-context.cpp
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Expand Up @@ -1317,8 +1317,8 @@ int llama_context::decode(llama_batch & inp_batch) {
n_outputs = n_outputs_new;
}

// non-causal masks do not use the KV cache
if (hparams.causal_attn) {
// find KV slot
{
kv_self_update();

// if we have enough unused cells before the current head ->
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160 changes: 63 additions & 97 deletions src/llama-graph.cpp
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Expand Up @@ -402,120 +402,86 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {

void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask || self_kq_mask_swa) {
// NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
if (cparams.causal_attn) {
const int64_t n_kv = kv_self->n;
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;

float * data = nullptr;
float * data_swa = nullptr;

if (self_kq_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask->buffer));
data = (float *) self_kq_mask->data;
}
const int64_t n_kv = kv_self->n;
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;

if (self_kq_mask_swa) {
GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask_swa->buffer));
data_swa = (float *) self_kq_mask_swa->data;
}
float * data = nullptr;
float * data_swa = nullptr;

// For causal attention, use only the previous KV cells
// of the correct sequence for each token of the ubatch.
// It's assumed that if a token in the batch has multiple sequences, they are equivalent.
for (int h = 0; h < 1; ++h) {
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
if (self_kq_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask->buffer));
data = (float *) self_kq_mask->data;
}

for (int j = 0; j < n_seq_tokens; ++j) {
const llama_pos pos = ubatch->pos[s*n_seq_tokens + j];
if (self_kq_mask_swa) {
GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask_swa->buffer));
data_swa = (float *) self_kq_mask_swa->data;
}

for (int i = 0; i < n_kv; ++i) {
float f;
if (!kv_self->cells[i].has_seq_id(seq_id) || kv_self->cells[i].pos > pos) {
f = -INFINITY;
// Use only the previous KV cells of the correct sequence for each token of the ubatch.
// It's assumed that if a token in the batch has multiple sequences, they are equivalent.
// Example with a cache of 10 tokens, 2 tokens populated in cache and 3 tokens in batch:
// Causal mask:
// xxx-------
// xxxx------
// xxxxx-----
// Non-causal mask:
// xxxxx-----
// xxxxx-----
// xxxxx-----
// To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
for (int h = 0; h < 1; ++h) {
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];

for (int j = 0; j < n_seq_tokens; ++j) {
const llama_pos pos = ubatch->pos[s*n_seq_tokens + j];
for (int i = 0; i < n_kv; ++i) {
float f;
// mask the token if:
if (!kv_self->cells[i].has_seq_id(seq_id) // not the correct sequence
|| (cparams.causal_attn && kv_self->cells[i].pos > pos) // for causal, mask future tokens
) {
f = -INFINITY;
} else {
if (hparams.use_alibi) {
f = -std::abs(kv_self->cells[i].pos - pos);
} else {
if (hparams.use_alibi) {
f = -std::abs(kv_self->cells[i].pos - pos);
} else {
f = 0.0f;
}
}

if (data) {
data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
f = 0.0f;
}
}

// may need to cut off old tokens for sliding window
if (data_swa) {
if (pos - kv_self->cells[i].pos >= (int32_t)hparams.n_swa) {
f = -INFINITY;
}
data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
}
if (data) {
data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
}
}
}

if (data) {
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
// may need to cut off old tokens for sliding window
if (data_swa) {
if (pos - kv_self->cells[i].pos >= (int32_t)hparams.n_swa) {
f = -INFINITY;
}
data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
}
}
}
}

if (data_swa) {
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
// mask padded tokens
if (data) {
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}
} else {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
// when using kv cache, the mask needs to match the kv cache size
const int64_t n_stride = n_tokens;

GGML_ASSERT(ggml_backend_buffer_is_host(self_kq_mask->buffer));

float * data = (float *) self_kq_mask->data;

for (int h = 0; h < 1; ++h) {
for (int s1 = 0; s1 < n_seqs; ++s1) {
const llama_seq_id seq_id = ubatch->seq_id[s1][0];

for (int j = 0; j < n_seq_tokens; ++j) {
const int32_t tj = s1*n_seq_tokens + j;

for (int s0 = 0; s0 < n_seqs; ++s0) {
for (int i = 0; i < n_seq_tokens; ++i) {
const int32_t ti = s0*n_seq_tokens + i;
float f = -INFINITY;

for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
if (ubatch->seq_id[s0][s] == seq_id) {
if (hparams.use_alibi) {
f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
} else {
f = 0.0f;
}
break;
}
}

data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
}
}

for (int i = n_tokens; i < n_stride; ++i) {
data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
}
// mask padded tokens
if (data_swa) {
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int j = 0; j < n_kv; ++j) {
data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}
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