ExternalVendorCode/whisper.cpp
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build : fix after master merge

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Georgi Gerganov 2023-09-06 14:27:51 +03:00
parent afc84b35b0
commit b0306cd5cf
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GPG Key ID: 449E073F9DC10735
2 changed files with 13 additions and 13 deletions
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1
examples/CMakeLists.txt
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@ -23,6 +23,7 @@ add_library(${TARGET} STATIC
common.cpp
common-ggml.h
common-ggml.cpp
grammar-parser.cpp
)
include(DefaultTargetOptions)

25
examples/talk-llama/llama.cpp
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@ -1164,7 +1164,7 @@ static bool llama_eval_internal(
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads) {
int n_threads) {
// enforce that the first token is BOS
if (n_past == 0 && tokens[0] != llama_token_bos()) {
@ -1190,6 +1190,8 @@ static bool llama_eval_internal(
const int n_vocab = hparams.n_vocab;
const int n_rot = hparams.n_embd/hparams.n_head;
const float eps = 5e-6f; // TODO: take from hparams
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
@ -1204,7 +1206,7 @@ static bool llama_eval_internal(
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
ggml_cgraph gf = {};
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
@ -1221,7 +1223,7 @@ static bool llama_eval_internal(
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
cur = ggml_rms_norm(ctx0, inpL, eps);
// cur = cur*attention_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
@ -1329,7 +1331,7 @@ static bool llama_eval_internal(
{
// norm
{
cur = ggml_rms_norm(ctx0, inpFF);
cur = ggml_rms_norm(ctx0, inpFF, eps);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
@ -1367,7 +1369,7 @@ static bool llama_eval_internal(
// norm
{
inpL = ggml_rms_norm(ctx0, inpL);
inpL = ggml_rms_norm(ctx0, inpL, eps);
// inpL = inpL*norm(broadcasted)
inpL = ggml_mul(ctx0, inpL, model.norm);
@ -1384,8 +1386,8 @@ static bool llama_eval_internal(
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_build_forward_expand (&gf, inpL);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
@ -2488,8 +2490,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
}
struct ggml_cgraph gf = ggml_build_forward(r);
gf.n_threads = n_threads;
ggml_graph_compute(lora_ctx, &gf);
ggml_graph_compute_with_ctx(lora_ctx, &gf, n_threads);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
@ -2635,7 +2636,6 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kout3d->data = out;
@ -2655,7 +2655,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
ggml_free(cpy_ctx);
}
@ -2743,7 +2743,6 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kin3d->data = (void *) inp;
@ -2763,7 +2762,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
ggml_free(cpy_ctx);
}