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Merge pull request #229 from agray3/ag_ggml_graph_caching
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Ag ggml graph caching
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@Nexesenex
Nexesenex authored Jul 11, 2024
2 parents 9925ca4 + a34900a commit 36bec6d
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Showing 3 changed files with 160 additions and 8 deletions.
5 changes: 5 additions & 0 deletions ggml/include/ggml-backend.h
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Original file line number Diff line number Diff line change
Expand Up @@ -230,6 +230,11 @@ extern "C" {
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);

// Utility to query whether cached GGML graph is in use
GGML_API bool ggml_use_cached_graph(ggml_backend_sched_t sched);

// Set whether or not to use GGML graph caching
GGML_API void ggml_set_cached_graph(ggml_backend_sched_t sched, bool set_value);

#ifdef __cplusplus
}
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33 changes: 32 additions & 1 deletion ggml/src/ggml-backend.c
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Original file line number Diff line number Diff line change
Expand Up @@ -1036,6 +1036,13 @@ struct ggml_backend_sched_split {
struct ggml_cgraph graph;
};

// Object to facilitate GML graph caching
struct ggml_cached_graph {
bool is_active;
ggml_backend_t input_backend;
struct ggml_tensor * input_cpy[GGML_SCHED_MAX_SPLIT_INPUTS];
};

struct ggml_backend_sched {
bool is_reset; // true if the scheduler has been reset since the last graph split
bool is_alloc;
Expand Down Expand Up @@ -1087,6 +1094,8 @@ struct ggml_backend_sched {
__attribute__((aligned(GGML_MEM_ALIGN)))
#endif
char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];

struct ggml_cached_graph cached_graph;
};

#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
Expand Down Expand Up @@ -1753,6 +1762,14 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];

if (!sched->cached_graph.is_active) {
sched->cached_graph.input_backend = input_backend;
sched->cached_graph.input_cpy[j] = input_cpy;
}
else {
input_backend = sched->cached_graph.input_backend;
input_cpy = sched->cached_graph.input_cpy[j];
}
if (input->flags & GGML_TENSOR_FLAG_INPUT) {
// inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
Expand Down Expand Up @@ -1872,6 +1889,8 @@ ggml_backend_sched_t ggml_backend_sched_new(

ggml_backend_sched_reset(sched);

sched->cached_graph.is_active = false;

return sched;
}

Expand Down Expand Up @@ -1947,6 +1966,9 @@ enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, st
}

enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {

if(!sched->cached_graph.is_active)
{
if (!sched->is_reset && !sched->is_alloc) {
ggml_backend_sched_reset(sched);
}
Expand All @@ -1956,7 +1978,7 @@ enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sch
return GGML_STATUS_ALLOC_FAILED;
}
}

}
return ggml_backend_sched_compute_splits(sched);
}

Expand Down Expand Up @@ -2223,3 +2245,12 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t

return true;
}

bool ggml_use_cached_graph(ggml_backend_sched_t sched) {
return sched->cached_graph.is_active;
}

void ggml_set_cached_graph(ggml_backend_sched_t sched, bool set_value) {
sched->cached_graph.is_active = set_value;
}

130 changes: 123 additions & 7 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2712,6 +2712,17 @@ struct llama_model {
}
};

// Object used to allow caching of GGML graph between tokens where possible.
struct ggml_cached_graph {
bool is_active = false;
ggml_cgraph * gf;
size_t n;
ggml_backend_t backend_res;
ggml_backend_t backend_embd;
struct ggml_tensor * res;
struct ggml_tensor * embd;
};

struct llama_context {
llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
~llama_context() {
Expand Down Expand Up @@ -2813,6 +2824,8 @@ struct llama_context {

// control vectors
struct llama_control_vector cvec;

struct ggml_cached_graph cached_graph;
};

static size_t llama_get_device_count(const llama_model & model) {
Expand Down Expand Up @@ -14524,12 +14537,44 @@ static int llama_decode_internal(
ggml_backend_sched_reset(lctx.sched);
ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);

ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);

ggml_cgraph * gf;
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
struct ggml_tensor * res;
struct ggml_tensor * embd;

bool n_has_changed_since_last_token = false;
if(lctx.cached_graph.n != kv_self.n) n_has_changed_since_last_token = true;
lctx.cached_graph.n = kv_self.n;

// Re-build graph only if graph caching is not possible
if(!ggml_use_cached_graph(lctx.sched) || n_has_changed_since_last_token) {

gf = llama_build_graph(lctx, u_batch, false);

// Set whether GGML graph caching is in use within GGML module, based on
// whether caching was activated here during the previous token
ggml_set_cached_graph(lctx.sched,lctx.cached_graph.is_active);

// Disable future graph caching in presence of env var,
// if there are multiple devices, if batch size is greater than 1,
// or if nsplits is not 2.
// TO DO enable graph caching for these cases
bool disable_cached_ggml_graph = (getenv("GGML_DISABLE_GRAPH_CACHING") != nullptr)
|| (llama_get_device_count(model) > 1)
|| (ggml_backend_sched_get_n_splits(lctx.sched) != 2);
for (int i = 0 ; i < gf->n_nodes; i++) {
if (gf->nodes[i]->op == GGML_OP_ADD && gf->nodes[i]->src[1] && gf->nodes[i]->src[1]->ne[1] > 1) {
disable_cached_ggml_graph = true;
break;
}
}

// Set whether graph caching should be used for future tokens
lctx.cached_graph.is_active=!disable_cached_ggml_graph;

// the output is always the last tensor in the graph
res = gf->nodes[gf->n_nodes - 1];
embd = gf->nodes[gf->n_nodes - 2];
if (lctx.n_outputs == 0) {
// no output
res = nullptr;
Expand All @@ -14545,10 +14590,71 @@ static int llama_decode_internal(
embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
}
lctx.cached_graph.res = res;
lctx.cached_graph.embd = embd;
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);

ggml_backend_sched_alloc_graph(lctx.sched, gf);

}
else {
gf = lctx.cached_graph.gf;
res = lctx.cached_graph.res;
embd = lctx.cached_graph.embd;
}
lctx.cached_graph.gf = gf;

if(ggml_use_cached_graph(lctx.sched)) {

// If using flash attention, find mask node so it can be skipped when updating
// KV cache paramaters in cached graph nodes below
void * flash_attn_mask_node = nullptr;
if(cparams.flash_attn) {
for (int i = 0; i < gf->n_nodes; i++) {
ggml_tensor * node = gf->nodes[i];
if (node->op == GGML_OP_FLASH_ATTN_EXT) {
flash_attn_mask_node = node->src[3];
break;
}
}
}

// Temporarily store KV cache parameters that will need updated in cached graph.
const struct llama_hparams & hparams = model.hparams;
const int64_t n_layer = hparams.n_layer;
const int64_t kv_head = kv_self.head;
std::vector<void *> kv_cache_ptrs;
for (int il = 0; il < n_layer; ++il) {
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
ggml_tensor * tmp_tensor = kv_self.k_l[il];
size_t tmp_offset = (ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa))*kv_head;
kv_cache_ptrs.push_back(static_cast<char*>(tmp_tensor->data) + tmp_offset);
tmp_tensor = kv_self.v_l[il];
if (cparams.flash_attn) {
tmp_offset = (kv_head)*ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
} else {
tmp_offset = (kv_head)*ggml_element_size(kv_self.v_l[il]);
}
kv_cache_ptrs.push_back(static_cast<char*>(tmp_tensor->data) + tmp_offset);
}

// Update KV cache parameters in cached graph.
int copy_op_count = 0;
if(gf != nullptr && gf->nodes != nullptr){
for (int i = 0; i < gf->n_nodes; i++) {
ggml_tensor * node = gf->nodes[i];
if (node->op == GGML_OP_CPY) {
if (node != flash_attn_mask_node) {
node->src[1]->data = kv_cache_ptrs[copy_op_count];
copy_op_count++;
}
}
}
}

}

llama_set_inputs(lctx, u_batch);

llama_graph_compute(lctx, gf, n_threads);
Expand All @@ -14571,11 +14677,15 @@ static int llama_decode_internal(
// extract logits
if (res) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
GGML_ASSERT(backend_res != nullptr);
GGML_ASSERT(lctx.logits != nullptr);

float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
const int32_t n_outputs_new = lctx.n_outputs;
if(!ggml_use_cached_graph(lctx.sched))
lctx.cached_graph.backend_res = backend_res;
else
backend_res = lctx.cached_graph.backend_res;

GGML_ASSERT(backend_res != nullptr);
GGML_ASSERT(lctx.logits != nullptr);

if (n_outputs_new) {
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
Expand All @@ -14587,6 +14697,12 @@ static int llama_decode_internal(
// extract embeddings
if (embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);


if(!ggml_use_cached_graph(lctx.sched))
lctx.cached_graph.backend_embd = backend_embd;
else
backend_embd = lctx.cached_graph.backend_embd;
GGML_ASSERT(backend_embd != nullptr);

switch (cparams.pooling_type) {
Expand Down

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