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gemma2: add sliding window mask #8227

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Jul 1, 2024
Merged

gemma2: add sliding window mask #8227

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7df7530
gemma2: add sliding window mask
ngxson Jun 30, 2024
ab2c3de
fix data_swa uninitialized
ngxson Jun 30, 2024
46b56e6
better naming
ngxson Jun 30, 2024
231dae4
add co-author
ngxson Jun 30, 2024
d09ecb8
replace list with single tensor
ngxson Jun 30, 2024
ed5496f
update
ngxson Jul 1, 2024
ce711f6
llama : minor styling
ggerganov Jul 1, 2024
7dc9cbf
convert : add sanity check for query_pre_attn_scalar
ggerganov Jul 1, 2024
80bdc38
Merge branch 'master' into xsn/gemma2_mask_swa
ngxson Jul 1, 2024
e24328e
fix small typo in README
ngxson Jul 1, 2024
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1 change: 1 addition & 0 deletions convert-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2369,6 +2369,7 @@ def set_gguf_parameters(self):
self.gguf_writer.add_final_logit_softcapping(
self.hparams["final_logit_softcapping"]
)
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unusem
Expand Down
1 change: 1 addition & 0 deletions gguf-py/gguf/constants.py
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Expand Up @@ -66,6 +66,7 @@ class Attention:
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
SLIDING_WINDOW = "{arch}.attention.sliding_window"

class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
Expand Down
3 changes: 3 additions & 0 deletions gguf-py/gguf/gguf_writer.py
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Expand Up @@ -552,6 +552,9 @@ def add_kv_lora_rank(self, length: int) -> None:
def add_relative_attn_buckets_count(self, value: int) -> None:
self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)

def add_sliding_window(self, value: int) -> None:
self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)

def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)

Expand Down
48 changes: 39 additions & 9 deletions src/llama.cpp
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Expand Up @@ -317,6 +317,7 @@ enum llm_kv {
LLM_KV_ATTENTION_Q_LORA_RANK,
LLM_KV_ATTENTION_KV_LORA_RANK,
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,

LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
Expand Down Expand Up @@ -409,6 +410,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },

{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
Expand Down Expand Up @@ -2099,6 +2101,7 @@ struct llama_hparams {
uint32_t n_ff_shexp = 0;
uint32_t n_expert_shared = 0;
float expert_weights_scale = 0.0;
uint32_t n_swa = 0; // sliding window attention (SWA)

float f_norm_eps;
float f_norm_rms_eps;
Expand Down Expand Up @@ -2661,6 +2664,9 @@ struct llama_context {
struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch]

// KQ mask per layer, used by sliding window attention (gemma 2)
struct ggml_tensor * inp_KQ_mask_swa;

// control vectors
struct llama_control_vector cvec;
};
Expand Down Expand Up @@ -4709,6 +4715,8 @@ static void llm_load_hparams(
} break;
case LLM_ARCH_GEMMA2:
{
hparams.n_swa = 4096; // default value of gemma 2
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
Expand Down Expand Up @@ -7786,6 +7794,7 @@ struct llm_build_context {
lctx.inp_s_copy = nullptr;
lctx.inp_s_mask = nullptr;
lctx.inp_s_seq = nullptr;
lctx.inp_KQ_mask_swa = nullptr;
}

void free() {
Expand Down Expand Up @@ -7938,15 +7947,18 @@ struct llm_build_context {
return lctx.inp_out_ids;
}

struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
if (causal) {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
struct ggml_tensor * build_inp_KQ_mask(bool causal = true, bool sliding_window = false) {
struct ggml_tensor * KQ_mask = causal
? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
: ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
cb(KQ_mask, "KQ_mask", -1);
ggml_set_input(KQ_mask);
if (sliding_window) {
lctx.inp_KQ_mask_swa = KQ_mask;
} else {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
lctx.inp_KQ_mask = KQ_mask;
}
cb(lctx.inp_KQ_mask, "KQ_mask", -1);
ggml_set_input(lctx.inp_KQ_mask);
return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
return flash_attn ? ggml_cast(ctx0, KQ_mask, GGML_TYPE_F16) : KQ_mask;
}

struct ggml_tensor * build_inp_mean() {
Expand Down Expand Up @@ -11029,9 +11041,14 @@ struct llm_build_context {
struct ggml_tensor * inp_pos = build_inp_pos();

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
// gemma 2 requires different mask for layers using sliding window (SWA)
struct ggml_tensor * KQ_mask_full = build_inp_KQ_mask(true, false);
struct ggml_tensor * KQ_mask_SWA = build_inp_KQ_mask(true, true);

for (int il = 0; il < n_layer; ++il) {
// (il % 2) layers use SWA
struct ggml_tensor * KQ_mask = (il % 2 == 0) ? KQ_mask_SWA : KQ_mask_full;

// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
Expand Down Expand Up @@ -12670,7 +12687,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {

GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));

float * data = (float *) lctx.inp_KQ_mask->data;
float * data = (float *) lctx.inp_KQ_mask->data;
float * data_swa = nullptr;

if (lctx.inp_KQ_mask_swa) {
data_swa = (float *) lctx.inp_KQ_mask_swa->data;
}

// For causal attention, use only the previous KV cells
// of the correct sequence for each token of the batch.
Expand All @@ -12692,6 +12714,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;

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

Expand Down
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