baalajimaestro/ollama
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

runner.go: Better abstract vision model integration

Browse source 
-Update mllama to take the cross attention state as embeddings in
a batch, more similar to how Llava handles it. This improves
integration with the input cache.
-Pass locations in a prompt for embeddings using tags similar to Llava.
-Abstract interface to vision models so the main runner accesses Clip
and Mllama similarly

Co-authored-by: Michael Yang <mxyng@pm.me>
This commit is contained in:
Jesse Gross 2024-10-11 15:34:01 -07:00 committed by Jesse Gross
parent 712e99d477
commit c826e57475
13 changed files with 534 additions and 454 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

93
llama/llama.cpp vendored
View file

@ -2699,7 +2699,7 @@ struct llama_hparams {
GGML_ABORT("fatal error");
}
bool cross_attention_layer(uint32_t il) const {
bool cross_attention_layers(uint32_t il) const {
return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
}
};
@ -2731,6 +2731,9 @@ struct llama_cparams {
bool offload_kqv;
bool flash_attn;
bool no_perf;
// TODO (jmorganca): this should most likely be passed in as part of a batch
// and not set on the context for all batches.
bool cross_attn = false;
enum llama_pooling_type pooling_type;
@ -3542,10 +3545,6 @@ struct llama_context {
struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
// TODO (jmorganca): this should most likely be passed in as part of a batch
// and not set on the context for all batches.
float * cross_attn_state = nullptr;
bool cross_attn_state_first_pass = true;
struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
};
@ -3782,7 +3781,7 @@ static bool llama_kv_cache_init(
for (int i = 0; i < (int) n_layer; i++) {
// for cross attention layers
if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layer(i)) {
if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
@ -7389,7 +7388,7 @@ static bool llm_load_tensors(
auto & layer = model.layers[i];
if (hparams.cross_attention_layer(i)) {
if (hparams.cross_attention_layers(i)) {
layer.cross_attn_k_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128});
layer.cross_attn_k_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024});
layer.cross_attn_o_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd});
@ -9368,11 +9367,10 @@ static struct ggml_tensor * llm_build_inp_cross_attn_state(
const llm_build_cb & cb) {
const int64_t n_embd = hparams.n_embd;
struct ggml_tensor * inpCAS;
lctx.inp_cross_attn_state = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
cb(lctx.inp_cross_attn_state, "inp_cross_attn_state", -1);
ggml_set_input(lctx.inp_cross_attn_state);
inpCAS = lctx.inp_cross_attn_state;
struct ggml_tensor * inpCAS = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
cb(inpCAS, "inp_cross_attn_state", -1);
ggml_set_input(inpCAS);
lctx.inp_cross_attn_state = inpCAS;
return inpCAS;
}
@ -10979,8 +10977,8 @@ struct llm_build_context {
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
if (hparams.cross_attention_layer(il)) {
if (!lctx.cross_attn_state) {
if (hparams.cross_attention_layers(il)) {
if (!batch.embd && !cparams.cross_attn) {
continue;
}
@ -10991,42 +10989,28 @@ struct llm_build_context {
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cb(Qcur, "Qcur", il);
Qcur = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
cb(Qcur, "Qcur", il);
// TODO: is this required?
Qcur = ggml_cont(ctx0, Qcur);
Qcur = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 0, 2, 1, 3));
cb(Qcur, "Qcur", il);
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].cross_attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur;
if (lctx.cross_attn_state_first_pass) {
struct ggml_tensor * Kcur, * Vcur;
if (batch.embd) {
Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS);
cb(Kcur, "Kcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, 6404);
cb(Kcur, "Kcur", il);
Kcur = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
cb(Kcur, "Kcur", il);
// TODO: is this required?
Kcur = ggml_cont(ctx0, Kcur);
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
cb(Kcur, "Kcur", il);
Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].cross_attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
cb(Kcur, "Kcur", il);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, kv_self.k_l[il]));
} else {
Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
cb(Kcur, "Kcur (view)", il);
}
struct ggml_tensor * Vcur;
if (lctx.cross_attn_state_first_pass) {
Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_v_proj, inpCAS);
cb(Vcur, "Vcur", il);
@ -11038,6 +11022,9 @@ struct llm_build_context {
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, kv_self.v_l[il]));
} else {
Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
cb(Kcur, "Kcur (view)", il);
Vcur = ggml_view_tensor(ctx0, kv_self.v_l[il]);
cb(Vcur, "Vcur (view)", il);
}
@ -11045,11 +11032,8 @@ struct llm_build_context {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, Kcur, Qcur);
cb(kq, "kq", il);
kq = ggml_scale_inplace(ctx0, kq, 1.0f/sqrtf(float(n_embd_head)));
cb(kq, "kq_scaled", il);
// TODO: apply causal masks
struct ggml_tensor * kq_soft_max = ggml_soft_max_inplace(ctx0, kq);
struct ggml_tensor * kq_soft_max = ggml_soft_max_ext(ctx0, kq, nullptr, 1.f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
cb(kq_soft_max, "kq_soft_max", il);
Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, Vcur));
@ -17197,11 +17181,20 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
if (batch.embd) {
if (lctx.inp_cross_attn_state && lctx.inp_cross_attn_state->buffer) {
ggml_backend_tensor_set(lctx.inp_cross_attn_state, batch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
// zero out inp_embd since it's not used
float * inp_embd_data = (float *)lctx.inp_embd->data;
for (int i = 0; i < ggml_nelements(lctx.inp_embd); ++i) {
inp_embd_data[i] = 0.0f;
}
} else {
const int64_t n_embd = hparams.n_embd;
const int64_t n_tokens = batch.n_tokens;
ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
}
}
if (batch.pos && lctx.inp_pos) {
const int64_t n_tokens = batch.n_tokens;
@ -17209,14 +17202,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
}
// TODO (jmorganca): this might copy a lot of data on every request of a
// single generation even though it doesn't change, so we should
// find a way to not set this more than one time per image
if (lctx.inp_cross_attn_state &&
lctx.inp_cross_attn_state->buffer) {
ggml_backend_tensor_set(lctx.inp_cross_attn_state, lctx.cross_attn_state, 0, hparams.n_embd * 1601 * 4 * ggml_element_size(lctx.inp_cross_attn_state));
}
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
const int64_t n_tokens = batch.n_tokens;
@ -17789,7 +17774,7 @@ static int llama_decode_internal(
n_outputs = 1;
}
lctx.sbatch.from_batch(batch_all, n_embd,
lctx.sbatch.from_batch(batch_all, batch_all.n_embd,
/* simple_split */ !kv_self.recurrent,
/* logits_all */ n_outputs == n_tokens_all);
@ -17899,10 +17884,6 @@ static int llama_decode_internal(
llama_set_inputs(lctx, ubatch);
// TODO: replace with something better to find out if its
// our first actual pass
lctx.cross_attn_state_first_pass = false;
llama_graph_compute(lctx, gf, n_threads, threadpool);
// update the kv ring buffer
@ -18086,7 +18067,7 @@ static int llama_encode_internal(
const int64_t n_embd = hparams.n_embd;
lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
lctx.sbatch.from_batch(batch, batch.n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
@ -20194,11 +20175,6 @@ struct llama_context * llama_new_context_with_model(
return ctx;
}
void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state) {
ctx->cross_attn_state_first_pass = true;
ctx->cross_attn_state = cross_attn_state;
}
void llama_free(struct llama_context * ctx) {
delete ctx;
}
@ -21686,6 +21662,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
void llama_set_cross_attention(struct llama_context * ctx, bool cross_attention) {
ctx->cparams.cross_attn = cross_attention;
}
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens,
@ -21695,6 +21675,7 @@ struct llama_batch llama_batch_get_one(
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
/*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@ -21710,6 +21691,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
/*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@ -21721,6 +21703,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
batch.n_embd = embd;
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}

145
llama/llama.go
View file

@ -111,6 +111,28 @@ func PrintSystemInfo() string {
return C.GoString(C.llama_print_system_info()) + compiler
}
func GetModelArch(modelPath string) (string, error) {
mp := C.CString(modelPath)
defer C.free(unsafe.Pointer(mp))
gguf_ctx := C.gguf_init_from_file(mp, C.struct_gguf_init_params{no_alloc: true, ctx: (**C.struct_ggml_context)(C.NULL)})
if gguf_ctx == nil {
return "", errors.New("unable to load model file")
}
defer C.gguf_free(gguf_ctx)
key := C.CString("general.architecture")
defer C.free(unsafe.Pointer(key))
arch_index := C.gguf_find_key(gguf_ctx, key)
if int(arch_index) < 0 {
return "", errors.New("unknown model architecture")
}
arch := C.gguf_get_val_str(gguf_ctx, arch_index)
return C.GoString(arch), nil
}
type ContextParams struct {
c C.struct_llama_context_params
}
@ -443,71 +465,36 @@ func Quantize(infile, outfile string, ftype uint32) error {
return nil
}
// llava
// vision processing
type ClipContext struct {
c *C.struct_clip_ctx
m *C.struct_mllama_ctx
IsMllama bool
embedPin runtime.Pinner
pinned bool
}
func getVisionArch(mp *C.char) (string, error) {
gguf_ctx := C.gguf_init_from_file(mp, C.struct_gguf_init_params{no_alloc: true, ctx: (**C.struct_ggml_context)(C.NULL)})
if gguf_ctx == nil {
return "", errors.New("unable to load vision projector")
}
defer C.gguf_free(gguf_ctx)
arch_index := C.gguf_find_key(gguf_ctx, C.CString("general.architecture"))
if int(arch_index) < 0 {
return "", errors.New("unknown vision model architecture")
}
arch := C.gguf_get_val_str(gguf_ctx, arch_index)
return C.GoString(arch), nil
}
func NewClipContext(modelPath string) (*ClipContext, error) {
func NewClipContext(llamaContext *Context, modelPath string) (*ClipContext, error) {
mp := C.CString(modelPath)
defer C.free(unsafe.Pointer(mp))
c := C.clip_model_load(mp, 1)
arch, err := getVisionArch(mp)
if err != nil {
return nil, err
projEmbedSize := int(C.clip_n_mmproj_embd(c))
modelEmbedSize := llamaContext.Model().NEmbd()
if projEmbedSize != modelEmbedSize {
return nil, fmt.Errorf("projector embedding size (%d) does not match model (%d)", projEmbedSize, modelEmbedSize)
}
var cc ClipContext
if arch == "clip" {
cc.c = C.clip_model_load(mp, 1)
} else if arch == "mllama" {
cc.m = C.mllama_model_load(mp, 1)
cc.IsMllama = true
} else {
return nil, fmt.Errorf("unknown vision model architecture: %s", arch)
}
// XXX: check embedding size?
return &cc, nil
return &ClipContext{c: c}, nil
}
func (c *ClipContext) Free() {
if c.c != nil {
C.clip_free(c.c)
}
if c.m != nil {
C.mllama_free(c.m)
}
}
func NewLlavaImageEmbed(llamaContext *Context, clipContext *ClipContext, data []byte) [][]float32 {
c := C.llava_image_embed_make_with_bytes(clipContext.c, C.int(llamaContext.numThreads), (*C.uchar)(unsafe.Pointer(&data[0])), C.int(len(data)))
func (c *ClipContext) NewEmbed(llamaContext *Context, data []byte) [][]float32 {
l := C.llava_image_embed_make_with_bytes(c.c, C.int(llamaContext.numThreads), (*C.uchar)(unsafe.Pointer(&data[0])), C.int(len(data)))
numTokens := int(c.n_image_pos)
numTokens := int(l.n_image_pos)
numEmbed := llamaContext.Model().NEmbd()
s := unsafe.Slice((*float32)(c.embed), numEmbed*numTokens)
s := unsafe.Slice((*float32)(l.embed), numEmbed*numTokens)
embed := make([][]float32, numTokens)
rows := make([]float32, len(s))
@ -517,51 +504,57 @@ func NewLlavaImageEmbed(llamaContext *Context, clipContext *ClipContext, data []
embed[i] = rows[i*numEmbed : (i+1)*numEmbed]
}
C.llava_image_embed_free(c)
C.llava_image_embed_free(l)
return embed
}
func NewMllamaImageEmbed(llamaContext *Context, clipContext *ClipContext, data []byte, aspectRatioId int) [][]float32 {
type MllamaContext struct {
c *C.struct_mllama_ctx
}
func NewMllamaContext(llamaContext *Context, modelPath string) (*MllamaContext, error) {
mp := C.CString(modelPath)
defer C.free(unsafe.Pointer(mp))
c := C.mllama_model_load(mp, 1)
projEmbedSize := int(C.mllama_n_embd(c))
modelEmbedSize := llamaContext.Model().NEmbd()
if projEmbedSize != modelEmbedSize {
return nil, fmt.Errorf("projector embedding size (%d) does not match model (%d)", projEmbedSize, modelEmbedSize)
}
return &MllamaContext{c: c}, nil
}
func (m *MllamaContext) Free() {
C.mllama_free(m.c)
}
func (m *MllamaContext) NewEmbed(llamaContext *Context, data []byte, aspectRatioId int) [][]float32 {
img := C.mllama_image_init()
defer C.mllama_image_free(img)
C.mllama_image_load_from_data(unsafe.Pointer(&data[0]), C.int(len(data)), 560, 560, 3, 4, C.int(aspectRatioId), img)
numTokens := int(C.mllama_n_positions(clipContext.m) * C.mllama_n_tiles(clipContext.m))
numEmbed := llamaContext.Model().NEmbd()
rows := make([]float32, m.EmbedSize(llamaContext))
C.mllama_image_encode(m.c, C.int(llamaContext.numThreads), img, (*C.float)(unsafe.Pointer(&rows[0])))
rows := make([]float32, numEmbed*numTokens)
C.mllama_image_encode(clipContext.m, C.int(llamaContext.numThreads), img, (*C.float)(unsafe.Pointer(&rows[0])))
embed := make([][]float32, numTokens)
for i := range embed {
embed[i] = rows[i*numEmbed : (i+1)*numEmbed]
}
embed := make([][]float32, 1)
embed[0] = rows
return embed
}
// This really needs to be set on a batch instead
func MllamaSetCrossAttn(llamaContext *Context, clipContext *ClipContext, embed [][]float32) {
if embed != nil {
if clipContext.pinned {
panic("Cross attention state already pinned")
}
func (m *MllamaContext) EmbedSize(llamaContext *Context) int {
numTokens := int(C.mllama_n_positions(m.c) * C.mllama_n_tiles(m.c))
numEmbed := llamaContext.Model().NEmbd()
embedData := &embed[0][0]
clipContext.embedPin.Pin(embedData)
clipContext.pinned = true
return numTokens * numEmbed
}
C.llama_set_cross_attn_state(llamaContext.c, (*C.float)(unsafe.Pointer(embedData)))
} else {
C.llama_set_cross_attn_state(llamaContext.c, (*C.float)(C.NULL))
if clipContext.pinned {
clipContext.embedPin.Unpin()
clipContext.pinned = false
}
}
func (c *Context) SetCrossAttention(state bool) {
C.llama_set_cross_attention(c.c, C.bool(state))
}
// sampling

3
llama/llama.h vendored
View file

@ -266,6 +266,7 @@ extern "C" {
llama_token * token;
float * embd;
int32_t n_embd;
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
@ -451,7 +452,7 @@ extern "C" {
// TODO (jmorganca): this should most likely be passed in as part of a batch
// and not set on the context for all batches.
LLAMA_API void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state);
LLAMA_API void llama_set_cross_attention(struct llama_context * ctx, bool cross_attn_state);
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);

2
llama/llava.cpp vendored
View file

@ -435,7 +435,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
if (n_eval > n_batch) {
n_eval = n_batch;
}
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), n_embd, nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;

238
llama/patches/0010-add-mllama-support.patch
View file

@ -12,27 +12,49 @@ kv cache once per run
remaining is to implement the cross attention mask
---
include/llama.h | 4 +
src/llama.cpp | 456 ++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 447 insertions(+), 13 deletions(-)
examples/llava/llava.cpp | 2 +-
include/llama.h | 5 +
src/llama.cpp | 447 +++++++++++++++++++++++++++++++++++++--
3 files changed, 436 insertions(+), 18 deletions(-)
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 8558c6bd..37b2f2e2 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -409,7 +409,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
if (n_eval > n_batch) {
n_eval = n_batch;
}
- llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
+ llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), n_embd, nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
diff --git a/include/llama.h b/include/llama.h
index 7cae1bbe..122e3cf1 100644
index 7cae1bbe..aca09310 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -423,6 +423,10 @@ extern "C" {
@@ -240,6 +240,7 @@ extern "C" {
llama_token * token;
float * embd;
+ int32_t n_embd;
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
@@ -423,6 +424,10 @@ extern "C" {
struct llama_model * model,
struct llama_context_params params);
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ LLAMA_API void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state);
+ LLAMA_API void llama_set_cross_attention(struct llama_context * ctx, bool cross_attn_state);
+
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
diff --git a/src/llama.cpp b/src/llama.cpp
index 83b80b59..b189a19a 100644
index 83b80b59..35748488 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -169,6 +169,7 @@ static std::string format(const char * fmt, ...) {
@ -160,13 +182,23 @@ index 83b80b59..b189a19a 100644
GGML_ABORT("fatal error");
}
+
+ bool cross_attention_layer(uint32_t il) const {
+ bool cross_attention_layers(uint32_t il) const {
+ return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
+ }
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2806,6 +2859,16 @@ struct llama_layer {
@@ -2652,6 +2705,9 @@ struct llama_cparams {
bool offload_kqv;
bool flash_attn;
bool no_perf;
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ bool cross_attn = false;
enum llama_pooling_type pooling_type;
@@ -2806,6 +2862,16 @@ struct llama_layer {
struct ggml_tensor * ffn_down_scale;
struct ggml_tensor * bskcn_tv;
@ -183,25 +215,21 @@ index 83b80b59..b189a19a 100644
};
// very similar to llama_batch,
@@ -3452,6 +3515,12 @@ struct llama_context {
@@ -3452,6 +3518,8 @@ struct llama_context {
struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ float * cross_attn_state = nullptr;
+ bool cross_attn_state_first_pass = true;
+ struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
};
struct llama_lora_weight {
@@ -3686,6 +3755,18 @@ static bool llama_kv_cache_init(
@@ -3686,6 +3754,18 @@ static bool llama_kv_cache_init(
cache.v_l.reserve(n_layer);
for (int i = 0; i < (int) n_layer; i++) {
+ // for cross attention layers
+ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layer(i)) {
+ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
+ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
+ ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
+ ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
@ -215,7 +243,7 @@ index 83b80b59..b189a19a 100644
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
@@ -5460,12 +5541,14 @@ static void llm_load_hparams(
@@ -5460,12 +5540,14 @@ static void llm_load_hparams(
}
// zero-out the per-layer hparams
@ -235,7 +263,7 @@ index 83b80b59..b189a19a 100644
// n_head_kv is optional, default to n_head
hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -5514,7 +5597,7 @@ static void llm_load_hparams(
@@ -5514,7 +5596,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
@ -244,7 +272,7 @@ index 83b80b59..b189a19a 100644
if (hparams.n_rot != hparams.n_embd_head_k) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
}
@@ -5554,6 +5637,16 @@ static void llm_load_hparams(
@@ -5554,6 +5636,16 @@ static void llm_load_hparams(
}
}
} break;
@ -261,7 +289,7 @@ index 83b80b59..b189a19a 100644
case LLM_ARCH_MINICPM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7249,6 +7342,55 @@ static bool llm_load_tensors(
@@ -7249,6 +7341,55 @@ static bool llm_load_tensors(
layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
}
} break;
@ -286,7 +314,7 @@ index 83b80b59..b189a19a 100644
+
+ auto & layer = model.layers[i];
+
+ if (hparams.cross_attention_layer(i)) {
+ if (hparams.cross_attention_layers(i)) {
+ layer.cross_attn_k_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128});
+ layer.cross_attn_k_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024});
+ layer.cross_attn_o_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd});
@ -317,7 +345,7 @@ index 83b80b59..b189a19a 100644
case LLM_ARCH_GROK:
{
if (n_expert == 0) {
@@ -9093,7 +9235,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
@@ -9093,7 +9234,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
@ -326,16 +354,7 @@ index 83b80b59..b189a19a 100644
}
if (params.vocab_only) {
@@ -9178,7 +9320,7 @@ static struct ggml_tensor * llm_build_inp_embd(
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
} else {
- lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+ lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
inpL = lctx.inp_embd;
ggml_set_input(lctx.inp_embd);
}
@@ -9193,6 +9335,22 @@ static struct ggml_tensor * llm_build_inp_embd(
@@ -9193,6 +9334,21 @@ static struct ggml_tensor * llm_build_inp_embd(
return inpL;
}
@ -346,11 +365,10 @@ index 83b80b59..b189a19a 100644
+ const llm_build_cb & cb) {
+ const int64_t n_embd = hparams.n_embd;
+
+ struct ggml_tensor * inpCAS;
+ lctx.inp_cross_attn_state = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
+ cb(lctx.inp_cross_attn_state, "inp_cross_attn_state", -1);
+ ggml_set_input(lctx.inp_cross_attn_state);
+ inpCAS = lctx.inp_cross_attn_state;
+ struct ggml_tensor * inpCAS = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
+ cb(inpCAS, "inp_cross_attn_state", -1);
+ ggml_set_input(inpCAS);
+ lctx.inp_cross_attn_state = inpCAS;
+
+ return inpCAS;
+}
@ -358,7 +376,7 @@ index 83b80b59..b189a19a 100644
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
@@ -10167,6 +10325,7 @@ struct llm_build_context {
@@ -10167,6 +10323,7 @@ struct llm_build_context {
lctx.inp_pos_bucket = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
@ -366,7 +384,7 @@ index 83b80b59..b189a19a 100644
}
void free() {
@@ -10754,6 +10913,253 @@ struct llm_build_context {
@@ -10754,6 +10911,239 @@ struct llm_build_context {
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
@ -410,8 +428,8 @@ index 83b80b59..b189a19a 100644
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ if (hparams.cross_attention_layer(il)) {
+ if (!lctx.cross_attn_state) {
+ if (hparams.cross_attention_layers(il)) {
+ if (!batch.embd && !cparams.cross_attn) {
+ continue;
+ }
+
@ -422,42 +440,28 @@ index 83b80b59..b189a19a 100644
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ cb(Qcur, "Qcur", il);
+
+ Qcur = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ cb(Qcur, "Qcur", il);
+
+ // TODO: is this required?
+ Qcur = ggml_cont(ctx0, Qcur);
+ Qcur = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 0, 2, 1, 3));
+ cb(Qcur, "Qcur", il);
+
+ Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].cross_attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur;
+ if (lctx.cross_attn_state_first_pass) {
+ struct ggml_tensor * Kcur, * Vcur;
+ if (batch.embd) {
+ Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS);
+ cb(Kcur, "Kcur", il);
+
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, 6404);
+ cb(Kcur, "Kcur", il);
+
+ Kcur = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
+ cb(Kcur, "Kcur", il);
+
+ // TODO: is this required?
+ Kcur = ggml_cont(ctx0, Kcur);
+ Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+ cb(Kcur, "Kcur", il);
+
+ Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].cross_attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
+ cb(Kcur, "Kcur", il);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, kv_self.k_l[il]));
+ } else {
+ Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
+ cb(Kcur, "Kcur (view)", il);
+ }
+
+ struct ggml_tensor * Vcur;
+ if (lctx.cross_attn_state_first_pass) {
+ Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_v_proj, inpCAS);
+ cb(Vcur, "Vcur", il);
+
@ -469,6 +473,9 @@ index 83b80b59..b189a19a 100644
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, kv_self.v_l[il]));
+ } else {
+ Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
+ cb(Kcur, "Kcur (view)", il);
+
+ Vcur = ggml_view_tensor(ctx0, kv_self.v_l[il]);
+ cb(Vcur, "Vcur (view)", il);
+ }
@ -476,11 +483,8 @@ index 83b80b59..b189a19a 100644
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, Kcur, Qcur);
+ cb(kq, "kq", il);
+
+ kq = ggml_scale_inplace(ctx0, kq, 1.0f/sqrtf(float(n_embd_head)));
+ cb(kq, "kq_scaled", il);
+
+ // TODO: apply causal masks
+ struct ggml_tensor * kq_soft_max = ggml_soft_max_inplace(ctx0, kq);
+ struct ggml_tensor * kq_soft_max = ggml_soft_max_ext(ctx0, kq, nullptr, 1.f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
+ cb(kq_soft_max, "kq_soft_max", il);
+
+ Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, Vcur));
@ -620,7 +624,7 @@ index 83b80b59..b189a19a 100644
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
@@ -16501,6 +16907,10 @@ static struct ggml_cgraph * llama_build_graph(
@@ -16501,6 +16891,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
@ -631,33 +635,48 @@ index 83b80b59..b189a19a 100644
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@@ -16773,6 +17183,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
@@ -16761,10 +17155,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
+ // TODO (jmorganca): this might copy a lot of data on every request of a
+ // single generation even though it doesn't change, so we should
+ // find a way to not set this more than one time per image
+ if (lctx.inp_cross_attn_state &&
+ lctx.inp_cross_attn_state->buffer) {
+ ggml_backend_tensor_set(lctx.inp_cross_attn_state, lctx.cross_attn_state, 0, hparams.n_embd * 1601 * 4 * ggml_element_size(lctx.inp_cross_attn_state));
if (batch.embd) {
- const int64_t n_embd = hparams.n_embd;
- const int64_t n_tokens = batch.n_tokens;
+ if (lctx.inp_cross_attn_state && lctx.inp_cross_attn_state->buffer) {
+ ggml_backend_tensor_set(lctx.inp_cross_attn_state, batch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
+ // zero out inp_embd since it's not used
+ float * inp_embd_data = (float *)lctx.inp_embd->data;
+ for (int i = 0; i < ggml_nelements(lctx.inp_embd); ++i) {
+ inp_embd_data[i] = 0.0f;
+ }
+
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
const int64_t n_tokens = batch.n_tokens;
@@ -17455,6 +17873,10 @@ static int llama_decode_internal(
+ } else {
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_tokens = batch.n_tokens;
llama_set_inputs(lctx, ubatch);
- ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ }
}
+ // TODO: replace with something better to find out if its
+ // our first actual pass
+ lctx.cross_attn_state_first_pass = false;
+
llama_graph_compute(lctx, gf, n_threads, threadpool);
if (batch.pos && lctx.inp_pos) {
@@ -17345,7 +17748,7 @@ static int llama_decode_internal(
n_outputs = 1;
}
// update the kv ring buffer
@@ -18648,7 +19070,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
- lctx.sbatch.from_batch(batch_all, n_embd,
+ lctx.sbatch.from_batch(batch_all, batch_all.n_embd,
/* simple_split */ !kv_self.recurrent,
/* logits_all */ n_outputs == n_tokens_all);
@@ -17638,7 +18041,7 @@ static int llama_encode_internal(
const int64_t n_embd = hparams.n_embd;
- lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
+ lctx.sbatch.from_batch(batch, batch.n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
@@ -18648,7 +19051,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
@ -668,19 +687,7 @@ index 83b80b59..b189a19a 100644
}
size_t total_size_org = 0;
@@ -19744,6 +20168,11 @@ struct llama_context * llama_new_context_with_model(
return ctx;
}
+void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state) {
+ ctx->cross_attn_state_first_pass = true;
+ ctx->cross_attn_state = cross_attn_state;
+}
+
void llama_free(struct llama_context * ctx) {
delete ctx;
}
@@ -19814,6 +20243,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
@@ -19814,6 +20219,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
@ -688,3 +695,38 @@ index 83b80b59..b189a19a 100644
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
@@ -21230,6 +21636,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
+void llama_set_cross_attention(struct llama_context * ctx, bool cross_attention) {
+ ctx->cparams.cross_attn = cross_attention;
+}
+
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens,
@@ -21239,6 +21649,7 @@ struct llama_batch llama_batch_get_one(
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
+ /*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@@ -21254,6 +21665,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
+ /*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@@ -21265,6 +21677,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
+ batch.n_embd = embd;
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}

58
llama/runner/cache.go
View file

@ -2,7 +2,6 @@ package main
import (
"errors"
"hash/maphash"
"log/slog"
"reflect"
"time"
@ -20,10 +19,6 @@ type InputCache struct {
// optimize cache eviction for multiple users
multiUserCache bool
// cache of images to embeddings
images []imageCache
imageHash maphash.Hash
lc *llama.Context
}
@ -41,7 +36,6 @@ func NewInputCache(lc *llama.Context, kvSize int, numSlots int, multiUserCache b
numCtx: kvSize / numSlots,
slots: slots,
multiUserCache: multiUserCache,
images: make([]imageCache, numSlots),
lc: lc,
}
}
@ -211,55 +205,3 @@ func (c *InputCache) ShiftCacheSlot(slot *InputCacheSlot, numKeep int, numDiscar
}
slot.Inputs = slot.Inputs[:len(slot.Inputs)-numDiscard]
}
// Locking: Lookup and store operations on imageCache require a lock
// to be held that serializes these with each other. Hash does not
// require a lock nor they need to be serialized with InputCacheSlot.
type imageCache struct {
key uint64
val [][]float32
lastUsed time.Time
}
func (c *InputCache) HashImage(image []byte) uint64 {
c.imageHash.Reset()
_, _ = c.imageHash.Write(image)
return c.imageHash.Sum64()
}
var ErrImageNotFound = errors.New("image not found in cache")
func (c *InputCache) FindImage(hash uint64) ([][]float32, error) {
for i := range c.images {
if c.images[i].key == hash {
slog.Debug("loading image embeddings from cache", "entry", i)
c.images[i].lastUsed = time.Now()
return c.images[i].val, nil
}
}
return nil, ErrImageNotFound
}
func (c *InputCache) AddImage(hash uint64, embed [][]float32) {
best := time.Now()
var bestImage int
for i := range c.images {
if c.images[i].key == hash {
bestImage = i
break
}
if c.images[i].lastUsed.Compare(best) < 0 {
best = c.images[i].lastUsed
bestImage = i
}
}
slog.Debug("storing image embeddings in cache", "entry", bestImage, "used", c.images[bestImage].lastUsed)
c.images[bestImage].key = hash
c.images[bestImage].val = embed
c.images[bestImage].lastUsed = time.Now()
}

75
llama/runner/cache_test.go
View file

@ -1,7 +1,6 @@
package main
import (
"reflect"
"testing"
"time"
)
@ -228,77 +227,3 @@ func TestFindCacheSlot(t *testing.T) {
})
}
}
func TestImageCache(t *testing.T) {
cache := NewInputCache(nil, 2048, 4, false)
valA := [][]float32{{0.1, 0.2}, {0.3}}
valB := [][]float32{{0.4}, {0.5}, {0.6}}
valC := [][]float32{{0.7}}
valD := [][]float32{{0.8}}
valE := [][]float32{{0.9}}
// Empty cache
result, err := cache.FindImage(0x5adb61d31933a946)
if err != ErrImageNotFound {
t.Errorf("found result in empty cache: result %v, err %v", result, err)
}
// Insert A
cache.AddImage(0x5adb61d31933a946, valA)
result, err = cache.FindImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Insert B
cache.AddImage(0x011551369a34a901, valB)
result, err = cache.FindImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valB) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Replace B with C
cache.AddImage(0x011551369a34a901, valC)
result, err = cache.FindImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valC) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Evict A
cache.AddImage(0x756b218a517e7353, valB)
cache.AddImage(0x75e5e8d35d7e3967, valD)
cache.AddImage(0xd96f7f268ca0646e, valE)
result, err = cache.FindImage(0x5adb61d31933a946)
if reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0x756b218a517e7353)
if !reflect.DeepEqual(result, valB) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valC) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0x75e5e8d35d7e3967)
if !reflect.DeepEqual(result, valD) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.FindImage(0xd96f7f268ca0646e)
if !reflect.DeepEqual(result, valE) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
}

145
llama/runner/image.go Normal file
View file

@ -0,0 +1,145 @@
package main
import (
"errors"
"fmt"
"hash/maphash"
"log/slog"
"sync"
"time"
"github.com/ollama/ollama/llama"
)
const imageCacheSize = 4
type ImageContext struct {
// mu is required to be held when generating embeddings or accessing the cache
mu sync.Mutex
clip *llama.ClipContext
mllama *llama.MllamaContext
// cache of images to embeddings
images []imageCache
imageHash maphash.Hash
}
func NewImageContext(llamaContext *llama.Context, modelPath string) (*ImageContext, error) {
arch, err := llama.GetModelArch(modelPath)
if err != nil {
return nil, fmt.Errorf("unable to determine vision architecture: %w (%s)", err, modelPath)
}
var c ImageContext
if arch == "clip" {
c.clip, err = llama.NewClipContext(llamaContext, modelPath)
} else if arch == "mllama" {
c.mllama, err = llama.NewMllamaContext(llamaContext, modelPath)
} else {
return nil, fmt.Errorf("unknown vision model architecture: %s", arch)
}
if err != nil {
return nil, err
}
c.images = make([]imageCache, imageCacheSize)
return &c, nil
}
func (c *ImageContext) Free(modelPath string) {
if c == nil {
return
}
if c.clip != nil {
c.clip.Free()
}
if c.mllama != nil {
c.mllama.Free()
}
}
func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte, aspectRatioId int) [][]float32 {
if c == nil {
return nil
}
hash := c.hashImage(data)
c.mu.Lock()
defer c.mu.Unlock()
embed, err := c.findImage(hash)
if err != nil {
if c.mllama != nil {
embed = c.mllama.NewEmbed(llamaContext, data, aspectRatioId)
} else if c.clip != nil {
embed = c.clip.NewEmbed(llamaContext, data)
} else {
return nil
}
c.addImage(hash, embed)
}
return embed
}
func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
if c != nil && c.mllama != nil {
return c.mllama.EmbedSize(llamaContext)
} else {
return llamaContext.Model().NEmbd()
}
}
type imageCache struct {
key uint64
val [][]float32
lastUsed time.Time
}
func (c *ImageContext) hashImage(image []byte) uint64 {
c.imageHash.Reset()
_, _ = c.imageHash.Write(image)
return c.imageHash.Sum64()
}
var errImageNotFound = errors.New("image not found in cache")
func (c *ImageContext) findImage(hash uint64) ([][]float32, error) {
for i := range c.images {
if c.images[i].key == hash {
slog.Debug("loading image embeddings from cache", "entry", i)
c.images[i].lastUsed = time.Now()
return c.images[i].val, nil
}
}
return nil, errImageNotFound
}
func (c *ImageContext) addImage(hash uint64, embed [][]float32) {
best := time.Now()
var bestImage int
for i := range c.images {
if c.images[i].key == hash {
bestImage = i
break
}
if c.images[i].lastUsed.Compare(best) < 0 {
best = c.images[i].lastUsed
bestImage = i
}
}
slog.Debug("storing image embeddings in cache", "entry", bestImage, "used", c.images[bestImage].lastUsed)
c.images[bestImage].key = hash
c.images[bestImage].val = embed
c.images[bestImage].lastUsed = time.Now()
}

80
llama/runner/image_test.go Normal file
View file

@ -0,0 +1,80 @@
package main
import (
"reflect"
"testing"
)
func TestImageCache(t *testing.T) {
cache := ImageContext{images: make([]imageCache, 4)}
valA := [][]float32{{0.1, 0.2}, {0.3}}
valB := [][]float32{{0.4}, {0.5}, {0.6}}
valC := [][]float32{{0.7}}
valD := [][]float32{{0.8}}
valE := [][]float32{{0.9}}
// Empty cache
result, err := cache.findImage(0x5adb61d31933a946)
if err != errImageNotFound {
t.Errorf("found result in empty cache: result %v, err %v", result, err)
}
// Insert A
cache.addImage(0x5adb61d31933a946, valA)
result, err = cache.findImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Insert B
cache.addImage(0x011551369a34a901, valB)
result, err = cache.findImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valB) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Replace B with C
cache.addImage(0x011551369a34a901, valC)
result, err = cache.findImage(0x5adb61d31933a946)
if !reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valC) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
// Evict A
cache.addImage(0x756b218a517e7353, valB)
cache.addImage(0x75e5e8d35d7e3967, valD)
cache.addImage(0xd96f7f268ca0646e, valE)
result, err = cache.findImage(0x5adb61d31933a946)
if reflect.DeepEqual(result, valA) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0x756b218a517e7353)
if !reflect.DeepEqual(result, valB) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0x011551369a34a901)
if !reflect.DeepEqual(result, valC) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0x75e5e8d35d7e3967)
if !reflect.DeepEqual(result, valD) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
result, err = cache.findImage(0xd96f7f268ca0646e)
if !reflect.DeepEqual(result, valE) {
t.Errorf("failed to find expected value: result %v, err %v", result, err)
}
}

55
llama/runner/runner.go
View file

@ -190,57 +190,22 @@ func (s *Server) inputs(prompt string, images []ImageData) ([]input, error) {
return nil, fmt.Errorf("invalid image index: %d", n)
}
hash := s.cache.HashImage(images[imageIndex].Data)
// Vision models cannot be accessed concurrently
s.clip.mu.Lock()
embed, err := s.cache.FindImage(hash)
if err != nil {
embed = llama.NewLlavaImageEmbed(s.lc, s.clip.cc, images[imageIndex].Data)
s.cache.AddImage(hash, embed)
}
s.clip.mu.Unlock()
embed := s.image.NewEmbed(s.lc, images[imageIndex].Data, images[imageIndex].AspectRatioID)
for _, e := range embed {
inputs = append(inputs, input{embed: e})
}
}
}
if s.clip.cc != nil {
var embed [][]float32
if s.clip.cc.IsMllama && len(images) >= 1 {
hash := s.cache.HashImage(images[0].Data)
s.clip.mu.Lock()
var err error
embed, err = s.cache.FindImage(hash)
if err != nil {
embed = llama.NewMllamaImageEmbed(s.lc, s.clip.cc, images[0].Data, images[0].AspectRatioID)
s.cache.AddImage(hash, embed)
}
s.clip.mu.Unlock()
}
s.mu.Lock()
llama.MllamaSetCrossAttn(s.lc, s.clip.cc, embed)
s.mu.Unlock()
}
return inputs, nil
}
type clip struct {
cc *llama.ClipContext
mu sync.Mutex
}
type Server struct {
model *llama.Model
lc *llama.Context
// required for image embeddings
clip clip
image *ImageContext
batchSize int
@ -322,14 +287,12 @@ func flushPending(seq *Sequence) bool {
func (s *Server) removeSequence(seqIndex int, reason string) {
seq := s.seqs[seqIndex]
s.lc.SetCrossAttention(false)
flushPending(seq)
seq.doneReason = reason
close(seq.responses)
close(seq.embedding)
seq.cache.InUse = false
if s.clip.cc != nil {
llama.MllamaSetCrossAttn(s.lc, s.clip.cc, nil)
}
s.seqs[seqIndex] = nil
}
@ -341,7 +304,7 @@ func (s *Server) run(ctx context.Context) {
tokenBatch := llama.NewBatch(s.batchSize*len(s.seqs), 0, len(s.seqs))
defer tokenBatch.Free()
embedBatch := llama.NewBatch(s.batchSize*len(s.seqs), s.lc.Model().NEmbd(), len(s.seqs))
embedBatch := llama.NewBatch(s.batchSize*len(s.seqs), s.image.EmbedSize(s.lc), len(s.seqs))
defer embedBatch.Free()
for {
@ -642,12 +605,20 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
s.mu.Lock()
for i, sq := range s.seqs {
if sq == nil {
for _, input := range seq.inputs {
if input.embed != nil {
s.lc.SetCrossAttention(true)
break
}
}
seq.cache, seq.inputs, seq.numPast, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
if err != nil {
s.mu.Unlock()
http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
return
}
s.seqs[i] = seq
s.cond.Signal()
break
@ -815,7 +786,7 @@ func (s *Server) loadModel(
if ppath != "" {
var err error
s.clip.cc, err = llama.NewClipContext(ppath)
s.image, err = NewImageContext(s.lc, ppath)
if err != nil {
panic(err)
}

34
server/prompt.go
View file

@ -75,11 +75,16 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
currMsgIdx := n
for cnt, msg := range msgs[currMsgIdx:] {
prefix := ""
imgPrompt := ""
prompt := msg.Content
for _, i := range msg.Images {
var imgData llm.ImageData
if isMllama {
lastMsgIdx := len(msgs) - 1
for i := lastMsgIdx; i >= currMsgIdx; i-- {
if len(msgs[i].Images) > 0 {
data, aspectRatioID, err := imageproc.Preprocess(msgs[i].Images[0])
data, aspectRatioID, err := imageproc.Preprocess(i)
if err != nil {
return "", nil, err
}
@ -90,25 +95,19 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
return "", nil, err
}
imgData := llm.ImageData{
imgData = llm.ImageData{
ID: len(images),
Data: buf.Bytes(),
AspectRatioID: aspectRatioID,
}
msgs[i].Content = strings.TrimSpace("<|image|>" + msgs[i].Content)
images = append(images, imgData)
break
}
}
imgPrompt = "<|image|>"
} else {
for cnt, msg := range msgs[currMsgIdx:] {
prefix := ""
prompt := msg.Content
for _, i := range msg.Images {
imgData := llm.ImageData{
imgData = llm.ImageData{
ID: len(images),
Data: i,
}
imgPrompt = " "
}
imgTag := fmt.Sprintf("[img-%d]", imgData.ID)
if !strings.Contains(prompt, "[img]") {
@ -119,8 +118,7 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
images = append(images, imgData)
}
msgs[currMsgIdx+cnt].Content = strings.TrimSpace(prefix + " " + prompt)
}
msgs[currMsgIdx+cnt].Content = strings.TrimSpace(prefix + imgPrompt + prompt)
}
// truncate any messages that do not fit into the context window

10
server/prompt_test.go
View file

@ -249,7 +249,7 @@ func TestChatPrompt(t *testing.T) {
{Role: "user", Content: "How many hotdogs are in this image?", Images: []api.ImageData{imgBuf}},
},
expect: expect{
prompt: "<|image|>How many hotdogs are in this image? ",
prompt: "[img-0]<|image|>How many hotdogs are in this image? ",
images: [][]byte{imgBuf},
aspectRatioID: 1,
},
@ -264,7 +264,7 @@ func TestChatPrompt(t *testing.T) {
{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{imgBuf}},
},
expect: expect{
prompt: "You're a test, Harry! I-I'm a what? <|image|>A test. And a thumping good one at that, I'd wager. ",
prompt: "You're a test, Harry! I-I'm a what? [img-0]<|image|>A test. And a thumping good one at that, I'd wager. ",
images: [][]byte{imgBuf},
aspectRatioID: 1,
},
@ -279,8 +279,8 @@ func TestChatPrompt(t *testing.T) {
{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{imgBuf2}},
},
expect: expect{
prompt: "You're a test, Harry! I-I'm a what? <|image|>A test. And a thumping good one at that, I'd wager. ",
images: [][]byte{imgBuf2},
prompt: "[img-0]<|image|>You're a test, Harry! I-I'm a what? [img-1]<|image|>A test. And a thumping good one at that, I'd wager. ",
images: [][]byte{imgBuf, imgBuf2},
aspectRatioID: 1,
},
},
@ -294,7 +294,7 @@ func TestChatPrompt(t *testing.T) {
{Role: "user", Content: "Which ones have mustard?"},
},
expect: expect{
prompt: "<|image|>How many hotdogs are in this image? There are four hotdogs. Which ones have mustard? ",
prompt: "[img-0]<|image|>How many hotdogs are in this image? There are four hotdogs. Which ones have mustard? ",
images: [][]byte{imgBuf},
aspectRatioID: 1,
},

8
server/routes.go
View file

@ -205,7 +205,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
return
}
images[i] = llm.ImageData{Data: buf.Bytes(), AspectRatioID: aspectRatioID}
images[i] = llm.ImageData{ID: i, Data: buf.Bytes(), AspectRatioID: aspectRatioID}
} else {
images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
}
@ -239,11 +239,11 @@ func (s *Server) GenerateHandler(c *gin.Context) {
}
for _, i := range images {
imgPrompt := ""
if isMllama {
msgs = append(msgs, api.Message{Role: "user", Content: "<|image|>"})
} else {
msgs = append(msgs, api.Message{Role: "user", Content: fmt.Sprintf("[img-%d]", i.ID)})
imgPrompt = "<|image|>"
}
msgs = append(msgs, api.Message{Role: "user", Content: fmt.Sprintf("[img-%d]"+imgPrompt, i.ID)})
}
values.Messages = append(msgs, api.Message{Role: "user", Content: req.Prompt})