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llama : factor out ggml-alloc from graph graph build functions

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ggml-ci
This commit is contained in:
Georgi Gerganov 2023-10-28 19:54:28 +03:00
parent ff3bad83e2
commit 8b2420d249
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GPG Key ID: 449E073F9DC10735
2 changed files with 215 additions and 342 deletions
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2
ggml.h
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@ -709,7 +709,7 @@ extern "C" {
// Context tensor enumeration and lookup // Context tensor enumeration and lookup
GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_get_tensor (struct ggml_context * ctx, const char * name);
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);

553
llama.cpp
View File

@ -3092,7 +3092,8 @@ static bool llama_model_load(
static struct ggml_cgraph * llm_build_llama( static struct ggml_cgraph * llm_build_llama(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -3118,10 +3119,10 @@ static struct ggml_cgraph * llm_build_llama(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; const bool do_rope_shift = worst_case || kv_self.has_shift;
//printf("n_kv = %d\n", n_kv); //printf("n_kv = %d\n", n_kv);
@ -3142,11 +3143,6 @@ static struct ggml_cgraph * llm_build_llama(
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -3156,12 +3152,8 @@ static struct ggml_cgraph * llm_build_llama(
#endif #endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
}
} }
ggml_set_name(inpL, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -3186,59 +3178,23 @@ static struct ggml_cgraph * llm_build_llama(
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
// KQ_pos - contains the positions // KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
offload_func_kq(KQ_pos); offload_func_kq(KQ_pos);
ggml_set_name(KQ_pos, "KQ_pos"); ggml_set_name(KQ_pos, "KQ_pos");
ggml_allocr_alloc(lctx.alloc, KQ_pos);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) KQ_pos->data;
for (int i = 0; i < n_tokens; ++i) {
data[i] = batch.pos[i];
}
}
// shift the entire K-cache if needed // shift the entire K-cache if needed
if (do_rope_shift) { if (do_rope_shift) {
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
offload_func_kq(K_shift); offload_func_kq(K_shift);
ggml_set_name(K_shift, "K_shift"); ggml_set_name(K_shift, "K_shift");
ggml_allocr_alloc(lctx.alloc, K_shift);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) K_shift->data;
for (int i = 0; i < n_ctx; ++i) {
data[i] = kv_self.cells[i].delta;
}
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * tmp = struct ggml_tensor * tmp =
@ -3480,7 +3436,8 @@ static struct ggml_cgraph * llm_build_llama(
static struct ggml_cgraph * llm_build_baichaun( static struct ggml_cgraph * llm_build_baichaun(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -3506,10 +3463,10 @@ static struct ggml_cgraph * llm_build_baichaun(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; const bool do_rope_shift = worst_case || kv_self.has_shift;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
@ -3528,11 +3485,6 @@ static struct ggml_cgraph * llm_build_baichaun(
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -3542,12 +3494,8 @@ static struct ggml_cgraph * llm_build_baichaun(
#endif #endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
}
} }
ggml_set_name(inpL, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -3572,59 +3520,23 @@ static struct ggml_cgraph * llm_build_baichaun(
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
// KQ_pos - contains the positions // KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
offload_func_kq(KQ_pos); offload_func_kq(KQ_pos);
ggml_set_name(KQ_pos, "KQ_pos"); ggml_set_name(KQ_pos, "KQ_pos");
ggml_allocr_alloc(lctx.alloc, KQ_pos);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) KQ_pos->data;
for (int i = 0; i < n_tokens; ++i) {
data[i] = batch.pos[i];
}
}
// shift the entire K-cache if needed // shift the entire K-cache if needed
if (do_rope_shift) { if (do_rope_shift) {
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
offload_func_kq(K_shift); offload_func_kq(K_shift);
ggml_set_name(K_shift, "K_shift"); ggml_set_name(K_shift, "K_shift");
ggml_allocr_alloc(lctx.alloc, K_shift);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) K_shift->data;
for (int i = 0; i < n_ctx; ++i) {
data[i] = kv_self.cells[i].delta;
}
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * tmp = struct ggml_tensor * tmp =
@ -3883,7 +3795,8 @@ static struct ggml_cgraph * llm_build_baichaun(
static struct ggml_cgraph * llm_build_refact( static struct ggml_cgraph * llm_build_refact(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -3905,8 +3818,8 @@ static struct ggml_cgraph * llm_build_refact(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
// printf("n_kv = %d\n", n_kv); // printf("n_kv = %d\n", n_kv);
@ -3927,11 +3840,6 @@ static struct ggml_cgraph * llm_build_refact(
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -3941,12 +3849,8 @@ static struct ggml_cgraph * llm_build_refact(
#endif #endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
}
} }
ggml_set_name(inpL, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -3971,34 +3875,12 @@ static struct ggml_cgraph * llm_build_refact(
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
ggml_format_name(inpL, "layer_inp_%d", il); ggml_format_name(inpL, "layer_inp_%d", il);
@ -4228,7 +4110,8 @@ static struct ggml_cgraph * llm_build_refact(
static struct ggml_cgraph * llm_build_falcon( static struct ggml_cgraph * llm_build_falcon(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -4254,10 +4137,10 @@ static struct ggml_cgraph * llm_build_falcon(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; const bool do_rope_shift = worst_case || kv_self.has_shift;
//printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n", //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
// kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift); // kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
@ -4279,11 +4162,6 @@ static struct ggml_cgraph * llm_build_falcon(
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -4293,12 +4171,8 @@ static struct ggml_cgraph * llm_build_falcon(
#endif #endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
}
} }
ggml_set_name(inpL, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -4323,59 +4197,23 @@ static struct ggml_cgraph * llm_build_falcon(
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
// KQ_pos - contains the positions // KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
offload_func_kq(KQ_pos); offload_func_kq(KQ_pos);
ggml_set_name(KQ_pos, "KQ_pos"); ggml_set_name(KQ_pos, "KQ_pos");
ggml_allocr_alloc(lctx.alloc, KQ_pos);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) KQ_pos->data;
for (int i = 0; i < n_tokens; ++i) {
data[i] = batch.pos[i];
}
}
// shift the entire K-cache if needed // shift the entire K-cache if needed
if (do_rope_shift) { if (do_rope_shift) {
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx); struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
offload_func_kq(K_shift); offload_func_kq(K_shift);
ggml_set_name(K_shift, "K_shift"); ggml_set_name(K_shift, "K_shift");
ggml_allocr_alloc(lctx.alloc, K_shift);
if (!ggml_allocr_is_measure(lctx.alloc)) {
int * data = (int *) K_shift->data;
for (int i = 0; i < n_ctx; ++i) {
data[i] = kv_self.cells[i].delta;
}
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * tmp = struct ggml_tensor * tmp =
@ -4595,7 +4433,8 @@ static struct ggml_cgraph * llm_build_falcon(
static struct ggml_cgraph * llm_build_starcoder( static struct ggml_cgraph * llm_build_starcoder(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -4619,8 +4458,8 @@ static struct ggml_cgraph * llm_build_starcoder(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
@ -4635,32 +4474,23 @@ static struct ggml_cgraph * llm_build_starcoder(
ggml_cgraph * gf = ggml_new_graph(ctx0); ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur; struct ggml_tensor * cur;
struct ggml_tensor * token; struct ggml_tensor * embd;
struct ggml_tensor * position; struct ggml_tensor * pos;
struct ggml_tensor * inpL; struct ggml_tensor * inpL;
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); embd = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
} else { } else {
#ifdef GGML_USE_MPI #ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented"); GGML_ASSERT(false && "not implemented");
#endif #endif
token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, token);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
}
} }
ggml_set_name(embd, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -4684,51 +4514,22 @@ static struct ggml_cgraph * llm_build_starcoder(
#endif // GGML_USE_CUBLAS #endif // GGML_USE_CUBLAS
{ {
// Compute position embeddings. struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); ggml_set_name(inp_pos, "inp_pos");
ggml_allocr_alloc(lctx.alloc, inp_positions);
if (!ggml_allocr_is_measure(lctx.alloc)) {
for (int i = 0; i < n_tokens; ++i) {
((int32_t *) inp_positions->data)[i] = batch.pos[i];
}
}
ggml_set_name(inp_positions, "inp_positions");
position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions); pos = ggml_get_rows(ctx0, model.pos_embeddings, inp_pos);
} }
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) { inpL = ggml_add(ctx0, embd, pos);
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
inpL = ggml_add(ctx0, token, position);
ggml_set_name(inpL, "inpL"); ggml_set_name(inpL, "inpL");
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
@ -4904,7 +4705,8 @@ static struct ggml_cgraph * llm_build_starcoder(
static struct ggml_cgraph * llm_build_persimmon( static struct ggml_cgraph * llm_build_persimmon(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
@ -4930,10 +4732,10 @@ static struct ggml_cgraph * llm_build_persimmon(
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift; const bool do_rope_shift = worst_case || kv_self.has_shift;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = { struct ggml_init_params params = {
@ -4951,12 +4753,8 @@ static struct ggml_cgraph * llm_build_persimmon(
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
} else { } else {
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
@ -4976,7 +4774,7 @@ static struct ggml_cgraph * llm_build_persimmon(
if (!ggml_allocr_is_measure(lctx.alloc)) { if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head))); ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
} }
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
@ -5301,7 +5099,8 @@ static struct ggml_cgraph * llm_build_persimmon(
static struct ggml_cgraph * llm_build_bloom( static struct ggml_cgraph * llm_build_bloom(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -5323,8 +5122,8 @@ static struct ggml_cgraph * llm_build_bloom(
const float norm_eps = hparams.f_norm_eps; const float norm_eps = hparams.f_norm_eps;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
@ -5341,66 +5140,35 @@ static struct ggml_cgraph * llm_build_bloom(
ggml_cgraph * gf = ggml_new_graph(ctx0); ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur; struct ggml_tensor * cur;
struct ggml_tensor * token; struct ggml_tensor * embd;
struct ggml_tensor * inpL; struct ggml_tensor * inpL;
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); embd = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
} else { } else {
#ifdef GGML_USE_MPI #ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented"); GGML_ASSERT(false && "not implemented");
#endif #endif
token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, token);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
}
} }
ggml_set_name(embd, "embd");
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
// norm // norm
{ {
inpL = ggml_norm(ctx0, token, norm_eps); inpL = ggml_norm(ctx0, embd, norm_eps);
inpL = ggml_add(ctx0, ggml_mul(ctx0, inpL, model.tok_norm), model.tok_norm_b); inpL = ggml_add (ctx0, ggml_mul(ctx0, inpL, model.tok_norm), model.tok_norm_b);
} }
ggml_set_name(inpL, "inpL"); ggml_set_name(inpL, "inpL");
@ -5416,9 +5184,9 @@ static struct ggml_cgraph * llm_build_bloom(
// Self Attention // Self Attention
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv); cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd); struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd));
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd); struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd));
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa)); struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
struct ggml_tensor * Qcur = tmpq; struct ggml_tensor * Qcur = tmpq;
struct ggml_tensor * Kcur = tmpk; struct ggml_tensor * Kcur = tmpk;
@ -5543,7 +5311,8 @@ static struct ggml_cgraph * llm_build_bloom(
static struct ggml_cgraph * llm_build_mpt( static struct ggml_cgraph * llm_build_mpt(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch,
bool worst_case) {
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -5567,8 +5336,8 @@ static struct ggml_cgraph * llm_build_mpt(
const int n_gpu_layers = model.n_gpu_layers; const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
@ -5590,13 +5359,6 @@ static struct ggml_cgraph * llm_build_mpt(
//int warmup = 0; //int warmup = 0;
if (batch.token) { if (batch.token) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
//warmup = ((uint32_t*) inp_tokens->data)[0] == 0;
}
ggml_set_name(inp_tokens, "inp_tokens"); ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens); inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -5606,12 +5368,8 @@ static struct ggml_cgraph * llm_build_mpt(
#endif #endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
}
} }
ggml_set_name(inpL, "inp_embd");
const int i_gpu_start = n_layer - n_gpu_layers; const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start; (void) i_gpu_start;
@ -5636,34 +5394,12 @@ static struct ggml_cgraph * llm_build_mpt(
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); ggml_set_name(KQ_scale, "KQ_scale");
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
offload_func_kq(KQ_mask); offload_func_kq(KQ_mask);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
memset(data, 0, ggml_nbytes(KQ_mask));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm;
@ -5865,43 +5601,180 @@ static struct ggml_cgraph * llama_build_graph(
struct ggml_cgraph * result = NULL; struct ggml_cgraph * result = NULL;
// check if we should build the worst-case graph (for memory measurement)
const bool worst_case = ggml_allocr_is_measure(lctx.alloc);
switch (model.arch) { switch (model.arch) {
case LLM_ARCH_LLAMA: case LLM_ARCH_LLAMA:
{ {
result = llm_build_llama(lctx, batch); result = llm_build_llama(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_BAICHUAN: case LLM_ARCH_BAICHUAN:
{ {
result = llm_build_baichaun(lctx, batch); result = llm_build_baichaun(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_FALCON: case LLM_ARCH_FALCON:
{ {
result = llm_build_falcon(lctx, batch); result = llm_build_falcon(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_STARCODER: case LLM_ARCH_STARCODER:
{ {
result = llm_build_starcoder(lctx, batch); result = llm_build_starcoder(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_PERSIMMON: case LLM_ARCH_PERSIMMON:
{ {
result = llm_build_persimmon(lctx, batch); result = llm_build_persimmon(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_REFACT: case LLM_ARCH_REFACT:
{ {
result = llm_build_refact(lctx, batch); result = llm_build_refact(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_BLOOM: case LLM_ARCH_BLOOM:
{ {
result = llm_build_bloom(lctx, batch); result = llm_build_bloom(lctx, batch, worst_case);
} break; } break;
case LLM_ARCH_MPT: case LLM_ARCH_MPT:
{ {
result = llm_build_mpt(lctx, batch); result = llm_build_mpt(lctx, batch, worst_case);
} break; } break;
default: default:
GGML_ASSERT(false); GGML_ASSERT(false);
} }
// set input data to the graph
// inp_tokens
if (batch.token) {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "inp_tokens");
GGML_ASSERT(cur != nullptr);
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_tokens = cur->ne[0];
memcpy(cur->data, batch.token, n_tokens*ggml_element_size(cur));
}
} else { // inp_embd
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "inp_embd");
GGML_ASSERT(cur != nullptr);
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_embd = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
memcpy(cur->data, batch.embd, n_tokens*n_embd*ggml_element_size(cur));
}
}
// inp_pos
do {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "inp_pos");
if (cur == nullptr) {
break;
}
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_tokens = cur->ne[0];
int32_t * data = (int32_t *) cur->data;
for (int i = 0; i < n_tokens; ++i) {
data[i] = batch.pos[i];
}
}
} while (0);
// KQ_scale
do {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "KQ_scale");
if (cur == nullptr) {
break;
}
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_embd_head = lctx.model.hparams.n_embd_head();
ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
}
} while (0);
// KQ_mask
do {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "KQ_mask");
if (cur == nullptr) {
break;
}
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_kv = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
float * data = (float *) cur->data;
memset(data, 0, ggml_nbytes(cur));
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_kv; ++i) {
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
}
}
}
} while (0);
// KQ_pos
do {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "KQ_pos");
if (cur == nullptr) {
break;
}
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_tokens = cur->ne[0];
int32_t * data = (int32_t *) cur->data;
for (int i = 0; i < n_tokens; ++i) {
data[i] = batch.pos[i];
}
}
} while (0);
// K_shift
do {
struct ggml_tensor * cur = ggml_graph_get_tensor(result, "K_shift");
if (cur == nullptr) {
break;
}
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_ctx = cur->ne[0];
int32_t * data = (int32_t *) cur->data;
for (int i = 0; i < n_ctx; ++i) {
data[i] = lctx.kv_self.cells[i].delta;
}
}
} while (0);
return result; return result;
} }