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Code Issues Actions 11 Packages Projects Releases Wiki Activity

llama : custom attention mask + parallel decoding + no context swaps (#3228)

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* tests : verify that RoPE is "additive"

* llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask)

* ggml : ggml_rope now takes a vector with positions instead of n_past

* metal : add rope_f16 kernel + optimize cpy kernels

* llama : unified KV cache + batch inference API

* llama : add new llama_decode() API that works with llama_batch

* llama : add cell_max heuristic for more efficient kv_cache

* llama : extend llama_kv_cache API

* llama : more robust cell_max heuristic + wip shift

* metal : disable concurrency optimization

* llama : add llama_kv_cache_shift_seq + no more context swaps

* llama : apply K-cache roping for Falcon and Baichuan

* speculative : fix KV cache management

* parallel : example for serving multiple users in parallel

* parallel : disable hot-plug to avoid cache fragmentation

* fixes : speculative KV cache + llama worst-case graph

* llama : extend batch API to select which logits to output

* llama : fix worst case graph build

* ggml-cuda : update rope implementation for parallel decoding (#3254)

* ggml-cuda : update rope implementation for parallel decoding

* better solution for p0 computation

* fix rope

* simpler rope implementation

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* make : add parallel to build + fix static functions in llama.cpp

* simple : fix token counting

* parallel : various improvements

* llama : fix cell_max logic + rename functions

* parallel : try smaller batches when the KV cache is fragmented

* parallel : fix sequence termination criteria

* llama : silence errors KV cache errors

* parallel : remove new line from prompt

* parallel : process system prompt once + configurable paramters + llama API

* parallel : remove question with short answers

* parallel : count cache misses

* parallel : print misses on each request

* parallel : minor

* llama : fix n_kv to never become 0

* parallel : rename hot-plug to continuous-batching

* llama : improve llama_batch API + simplify parallel example

* simple : add parallel decoding support

* simple : improve comments + free batch

* ggml-cuda : add rope f16, restore performance with parallel decoding (#3272)

* ggml-cuda : add rope f16, restore performance

* offload KQ_mask with all models

* fix rope shift

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* llama : disable MPI for now

ggml-ci

* train : make KQ_pos memory buffer permanent via dummy scale op

* ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275)

ggml-ci

* parallel : fix bug (extra BOS) + smaller token_prev array

* parallel : fix cases where the input prompts can overflow the batch

* parallel : add disabled experimental batch chunking in powers of two

* llama : llama.h formatting + comments

* simple : add README.md

* llama : fix kv cache heuristic when context is less than 32

* parallel : fix crash when `-n -1`

* llama : simplify returns if/else branches

* metal : use mm kernels for batch size > 2

* examples : utilize new llama_get_logits_ith()

* examples : add example for batched decoding

* examples : do not eval prompt 2 times (close #3348)

* server : clear the KV cache beyond n_past before llama_decode

* server : avoid context swaps by shifting the KV cache

---------

Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov 2023-09-28 19:04:36 +03:00 committed by GitHub
parent 45855b3f1c
commit ec893798b7
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35 changed files with 2700 additions and 673 deletions
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2
.gitignore vendored
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@ -51,7 +51,9 @@ models-mnt
/save-load-state
/server
/simple
/batched
/speculative
/parallel
/train-text-from-scratch
/vdot
build-info.h

8
Makefile
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@ -1,5 +1,5 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple batched save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative parallel tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama
@ -519,6 +519,9 @@ main: examples/main/main.cpp build-info.h ggml.
simple: examples/simple/simple.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
batched: examples/batched/batched.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@ -565,6 +568,9 @@ beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o co
speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)

43
common/common.cpp
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@ -317,6 +317,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_chunks = std::stoi(argv[i]);
} else if (arg == "-np" || arg == "--parallel") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_parallel = std::stoi(argv[i]);
} else if (arg == "-ns" || arg == "--sequences") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_sequences = std::stoi(argv[i]);
} else if (arg == "-m" || arg == "--model") {
if (++i >= argc) {
invalid_param = true;
@ -360,6 +372,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.multiline_input = true;
} else if (arg == "--simple-io") {
params.simple_io = true;
} else if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
} else if (arg == "--color") {
params.use_color = true;
} else if (arg == "--mlock") {
@ -436,8 +450,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.use_mmap = false;
} else if (arg == "--numa") {
params.numa = true;
} else if (arg == "--export") {
params.export_cgraph = true;
} else if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
@ -456,8 +468,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
} else if (arg == "--perplexity") {
params.perplexity = true;
} else if (arg == "--perplexity" || arg == "--all-logits") {
params.logits_all = true;
} else if (arg == "--ppl-stride") {
if (++i >= argc) {
invalid_param = true;
@ -655,12 +667,15 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
printf(" --temp N temperature (default: %.1f)\n", (double)params.temp);
printf(" --perplexity compute perplexity over each ctx window of the prompt\n");
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
if (llama_mlock_supported()) {
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
@ -685,7 +700,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" Not recommended since this is both slower and uses more VRAM.\n");
#endif // GGML_USE_CUBLAS
#endif
printf(" --export export the computation graph to 'llama.ggml'\n");
printf(" --verbose-prompt print prompt before generation\n");
fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
@ -738,7 +752,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;
lparams.use_mlock = params.use_mlock;
lparams.logits_all = params.perplexity;
lparams.logits_all = params.logits_all;
lparams.embedding = params.embedding;
lparams.rope_freq_base = params.rope_freq_base;
lparams.rope_freq_scale = params.rope_freq_scale;
@ -782,8 +796,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
{
LOG("warming up the model with an empty run\n");
const std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
llama_eval(lctx, tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, params.n_threads);
std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0), params.n_threads);
llama_kv_cache_tokens_rm(lctx, -1, -1);
llama_reset_timings(lctx);
}
@ -890,7 +905,7 @@ llama_token llama_sample_token(
llama_token id = 0;
float * logits = llama_get_logits(ctx) + idx * n_vocab;
float * logits = llama_get_logits_ith(ctx, idx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
@ -941,11 +956,11 @@ llama_token llama_sample_token(
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &cur_p, temp);
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &cur_p, temp);
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
@ -953,7 +968,7 @@ llama_token llama_sample_token(
llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
llama_sample_typical (ctx, &cur_p, typical_p, 1);
llama_sample_top_p (ctx, &cur_p, top_p, 1);
llama_sample_temperature(ctx, &cur_p, temp);
llama_sample_temp(ctx, &cur_p, temp);
{
const int n_top = 10;
@ -1182,7 +1197,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
@ -1256,6 +1270,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);

8
common/common.h
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@ -42,6 +42,8 @@ struct gpt_params {
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
@ -107,16 +109,16 @@ struct gpt_params {
bool interactive_first = false; // wait for user input immediately
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool cont_batching = false; // insert new sequences for decoding on-the-fly
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool ignore_eos = false; // ignore generated EOS tokens
bool instruct = false; // instruction mode (used for Alpaca models)
bool penalize_nl = true; // consider newlines as a repeatable token
bool perplexity = false; // compute perplexity over the prompt
bool logits_all = false; // return logits for all tokens in the batch
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool numa = false; // attempt optimizations that help on some NUMA systems
bool export_cgraph = false; // export the computation graph
bool verbose_prompt = false; // print prompt tokens before generation
};
@ -181,7 +183,7 @@ std::string llama_detokenize_bpe(
// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
// - grammar: grammar to use for sampling, ignore if NULL
// - last_tokens: needed for repetition penalty, ignore if empty
// - idx: sample from llama_get_logits(ctx) + idx * n_vocab
// - idx: sample from llama_get_logits_ith(ctx, idx)
//
// returns:
// - token: sampled token

2
examples/CMakeLists.txt
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@ -23,7 +23,9 @@ else()
add_subdirectory(train-text-from-scratch)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
add_subdirectory(batched)
add_subdirectory(speculative)
add_subdirectory(parallel)
add_subdirectory(embd-input)
add_subdirectory(llama-bench)
add_subdirectory(beam-search)

37
examples/baby-llama/baby-llama.cpp
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@ -554,6 +554,14 @@ static struct ggml_tensor * forward(
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N,1,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
for (int il = 0; il < n_layer; ++il) {
@ -581,8 +589,8 @@ static struct ggml_tensor * forward(
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Kcur shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
// store key and value to memory
{
@ -808,9 +816,18 @@ static struct ggml_tensor * forward_batch(
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N*n_batch,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
assert_shape_2d(inpL, n_embd, N*n_batch);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
@ -838,8 +855,8 @@ static struct ggml_tensor * forward_batch(
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
// Kcur shape [n_embd/n_head, n_head, N, n_batch]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
@ -1097,6 +1114,14 @@ static struct ggml_tensor * forward_lora(
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N,1,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
for (int il = 0; il < n_layer; ++il) {
@ -1130,7 +1155,7 @@ static struct ggml_tensor * forward_lora(
model->layers[il].wqb,
cur)),
n_embd/n_head, n_head, N),
n_past, n_rot, 0, 0);
KQ_pos, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_mul_mat(ctx0,
@ -1139,7 +1164,7 @@ static struct ggml_tensor * forward_lora(
model->layers[il].wkb,
cur)),
n_embd/n_head, n_head, N),
n_past, n_rot, 0, 0);
KQ_pos, n_rot, 0, 0);
// store key and value to memory
{

5
examples/batched/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET batched)
add_executable(${TARGET} batched.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

44
examples/batched/README.md Normal file
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@ -0,0 +1,44 @@
# llama.cpp/example/batched
The example demonstrates batched generation from a given prompt
```bash
./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
...
main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113
Hello my name is
main: generating 4 sequences ...
main: stream 0 finished
main: stream 1 finished
main: stream 2 finished
main: stream 3 finished
sequence 0:
Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b
sequence 1:
Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between
sequence 2:
Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am
sequence 3:
Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and
main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s
llama_print_timings: load time = 587.00 ms
llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second)
llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second)
llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: total time = 4156.04 ms
```

246
examples/batched/batched.cpp Normal file
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@ -0,0 +1,246 @@
#include "common.h"
#include "llama.h"
#include <algorithm>
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL]\n" , argv[0]);
return 1 ;
}
int n_parallel = 1;
if (argc >= 2) {
params.model = argv[1];
}
if (argc >= 3) {
params.prompt = argv[2];
}
if (argc >= 4) {
n_parallel = std::atoi(argv[3]);
}
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
// total length of the sequences including the prompt
const int n_len = 32;
// init LLM
llama_backend_init(params.numa);
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = 1234;
ctx_params.n_ctx = n_len*n_parallel; // FIXME: use n_kv_req instead (tokenize with model after #3301)
ctx_params.n_batch = std::max(n_len, n_parallel);
// ctx_params.n_gpu_layers = 99; // offload all layers to the GPU
llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req);
LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
return 1;
}
// print the prompt token-by-token
fprintf(stderr, "\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
// create a llama_batch with size 512
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0);
// evaluate the initial prompt
batch.n_tokens = tokens_list.size();
for (int32_t i = 0; i < batch.n_tokens; i++) {
batch.token[i] = tokens_list[i];
batch.pos[i] = i;
batch.seq_id[i] = 0;
batch.logits[i] = false;
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx, batch, params.n_threads) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
// assign the system KV cache to all parallel sequences
// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
for (int32_t i = 1; i < n_parallel; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
}
if (n_parallel > 1) {
LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
}
// main loop
// we will store the parallel decoded sequences in this vector
std::vector<std::string> streams(n_parallel);
// remember the batch index of the last token for each parallel sequence
// we need this to determine which logits to sample from
std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
int n_cur = batch.n_tokens;
int n_decode = 0;
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
// prepare the next batch
batch.n_tokens = 0;
// sample the next token for each parallel sequence / stream
for (int32_t i = 0; i < n_parallel; ++i) {
if (i_batch[i] < 0) {
// the stream has already finished
continue;
}
auto n_vocab = llama_n_vocab(ctx);
auto * logits = llama_get_logits_ith(ctx, i_batch[i]);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
const int top_k = 40;
const float top_p = 0.9f;
const float temp = 0.4f;
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temp (ctx, &candidates_p, temp);
const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream? -> mark the stream as finished
if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
i_batch[i] = -1;
LOG_TEE("\n");
if (n_parallel > 1) {
LOG_TEE("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
}
continue;
}
// if there is only one stream, we print immediately to stdout
if (n_parallel == 1) {
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
}
streams[i] += llama_token_to_piece(ctx, new_token_id);
// push this new token for next evaluation
batch.token [batch.n_tokens] = new_token_id;
batch.pos [batch.n_tokens] = n_cur;
batch.seq_id[batch.n_tokens] = i;
batch.logits[batch.n_tokens] = true;
i_batch[i] = batch.n_tokens;
batch.n_tokens += 1;
n_decode += 1;
}
// all streams are finished
if (batch.n_tokens == 0) {
break;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
LOG_TEE("\n");
if (n_parallel > 1) {
LOG_TEE("\n");
for (int32_t i = 0; i < n_parallel; ++i) {
LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
}
}
const auto t_main_end = ggml_time_us();
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
llama_print_timings(ctx);
fprintf(stderr, "\n");
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

5
examples/beam-search/beam-search.cpp
View File

@ -158,8 +158,9 @@ int main(int argc, char ** argv)
}
std::cout << std::flush;
int n_past = llama_get_kv_cache_token_count(ctx);
if (llama_eval(ctx, tokens_list.data(), tokens_list.size(), n_past, params.n_threads))
int n_past = 0;
if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0), params.n_threads))
{
fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
return 1;

11
examples/embd-input/embd-input-lib.cpp
View File

@ -80,7 +80,8 @@ bool eval_float(void * model, float * input, int N){
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
llama_batch batch = { int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, };
if (llama_decode(ctx, batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
@ -101,7 +102,7 @@ bool eval_tokens(void * model, std::vector<llama_token> tokens) {
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&tokens[i], n_eval, n_past, 0), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
@ -183,11 +184,11 @@ llama_token sampling_id(struct MyModel* mymodel) {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
@ -195,7 +196,7 @@ llama_token sampling_id(struct MyModel* mymodel) {
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}

2
examples/embedding/embedding.cpp
View File

@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
while (!embd_inp.empty()) {
int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
if (llama_eval(ctx, embd_inp.data(), n_tokens, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}

8
examples/llama-bench/llama-bench.cpp
View File

@ -891,7 +891,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
int n_processed = 0;
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
llama_eval(ctx, tokens.data(), n_tokens, n_past + n_processed, n_threads);
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0), n_threads);
n_processed += n_tokens;
}
}
@ -899,7 +899,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
llama_token token = llama_token_bos(ctx);
for (int i = 0; i < n_gen; i++) {
llama_eval(ctx, &token, 1, n_past + i, n_threads);
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0), n_threads);
}
}
@ -977,6 +977,8 @@ int main(int argc, char ** argv) {
test t(inst, lmodel, ctx);
llama_kv_cache_tokens_rm(ctx, -1, -1);
// warmup run
if (t.n_prompt > 0) {
test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
@ -986,6 +988,8 @@ int main(int argc, char ** argv) {
}
for (int i = 0; i < params.reps; i++) {
llama_kv_cache_tokens_rm(ctx, -1, -1);
uint64_t t_start = get_time_ns();
if (t.n_prompt > 0) {
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);

36
examples/main/main.cpp
View File

@ -124,7 +124,7 @@ int main(int argc, char ** argv) {
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
if (params.perplexity) {
if (params.logits_all) {
printf("\n************\n");
printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
printf("************\n\n");
@ -200,15 +200,6 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
// export the cgraph and exit
if (params.export_cgraph) {
llama_eval_export(ctx, "llama.ggml");
llama_free(ctx);
llama_free_model(model);
return 0;
}
std::string path_session = params.path_prompt_cache;
std::vector<llama_token> session_tokens;
@ -508,18 +499,23 @@ int main(int argc, char ** argv) {
break;
}
const int n_left = n_past - params.n_keep;
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d\n", n_past, n_left, n_ctx, params.n_keep);
const int n_left = n_past - params.n_keep - 1;
const int n_discard = n_left/2;
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
n_past_guidance = std::max(1, params.n_keep + guidance_offset);
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
n_past -= n_discard;
if (ctx_guidance) {
n_past_guidance -= n_discard;
}
LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
// insert n_left/2 tokens at the start of embd from last_tokens
embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size());
LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
LOG("clear session path\n");
@ -580,7 +576,7 @@ int main(int argc, char ** argv) {
for (int i = 0; i < input_size; i += params.n_batch) {
int n_eval = std::min(input_size - i, params.n_batch);
if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) {
if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0), params.n_threads)) {
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}
@ -597,7 +593,7 @@ int main(int argc, char ** argv) {
LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0), params.n_threads)) {
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}

8
examples/parallel/CMakeLists.txt Normal file
View File

@ -0,0 +1,8 @@
set(TARGET parallel)
add_executable(${TARGET} parallel.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

3
examples/parallel/README.md Normal file
View File

@ -0,0 +1,3 @@
# llama.cpp/example/parallel
Simplified simluation for serving incoming requests in parallel

380
examples/parallel/parallel.cpp Normal file
View File

@ -0,0 +1,380 @@
// A basic application simulating a server with multiple clients.
// The clients submite requests to the server and they are processed in parallel.
#include "build-info.h"
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
// trim whitespace from the beginning and end of a string
static std::string trim(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && isspace(str[start])) {
start += 1;
}
while (end > start && isspace(str[end - 1])) {
end -= 1;
}
return str.substr(start, end - start);
}
static std::string k_system =
R"(Transcript of a never ending dialog, where the User interacts with an Assistant.
The Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
User: Recommend a nice restaurant in the area.
Assistant: I recommend the restaurant "The Golden Duck". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.
User: Who is Richard Feynman?
Assistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including "Surely You're Joking, Mr. Feynman!" and "What Do You Care What Other People Think?".
User:)";
static std::vector<std::string> k_prompts = {
"What is the meaning of life?",
"Tell me an interesting fact about llamas.",
"What is the best way to cook a steak?",
"Are you familiar with the Special Theory of Relativity and can you explain it to me?",
"Recommend some interesting books to read.",
"What is the best way to learn a new language?",
"How to get a job at Google?",
"If you could have any superpower, what would it be?",
"I want to learn how to play the piano.",
};
struct client {
int32_t id = 0;
llama_seq_id seq_id = -1;
llama_token sampled;
int64_t t_start_prompt;
int64_t t_start_gen;
int32_t n_prompt = 0;
int32_t n_decoded = 0;
int32_t i_batch = -1;
std::string input;
std::string prompt;
std::string response;
std::vector<llama_token> tokens_prev;
};
int main(int argc, char ** argv) {
srand(1234);
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
// number of simultaneous "clients" to simulate
const int32_t n_clients = params.n_parallel;
// requests to simulate
const int32_t n_seq = params.n_sequences;
// insert new requests as soon as the previous one is done
const bool cont_batching = params.cont_batching;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("parallel", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the target model
params.logits_all = true;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
fprintf(stderr, "\n\n");
fflush(stderr);
const int n_ctx = llama_n_ctx(ctx);
const int n_vocab = llama_n_vocab(ctx);
std::vector<client> clients(n_clients);
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.tokens_prev.resize(std::max(256, params.n_predict));
std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0);
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
std::vector<llama_token> tokens_system;
tokens_system = ::llama_tokenize(ctx, k_system, true);
const int32_t n_tokens_system = tokens_system.size();
llama_seq_id g_seq_id = 0;
// the max batch size is as large as the context to handle cases where we get very long input prompt from multiple
// users. regardless of the size, the main loop will chunk the batch into a maximum of params.n_batch tokens at a time
llama_batch batch = llama_batch_init(params.n_ctx, 0);
int32_t n_total_prompt = 0;
int32_t n_total_gen = 0;
int32_t n_cache_miss = 0;
const auto t_main_start = ggml_time_us();
LOG_TEE("%s: Simulating parallel requests from clients:\n", __func__);
LOG_TEE("%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system);
LOG_TEE("\n");
{
LOG_TEE("%s: Evaluating the system prompt ...\n", __func__);
batch.n_tokens = n_tokens_system;
for (int32_t i = 0; i < batch.n_tokens; ++i) {
batch.token[i] = tokens_system[i];
batch.pos[i] = i;
batch.seq_id[i] = 0;
batch.logits[i] = false;
}
if (llama_decode(ctx, batch, params.n_threads) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
// assign the system KV cache to all parallel sequences
for (int32_t i = 1; i < n_clients; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
}
LOG_TEE("\n");
}
LOG_TEE("Processing requests ...\n\n");
while (true) {
batch.n_tokens = 0;
// decode any currently ongoing sequences
for (auto & client : clients) {
if (client.seq_id == -1) {
continue;
}
batch.token [batch.n_tokens] = client.sampled;
batch.pos [batch.n_tokens] = n_tokens_system + client.n_prompt + client.n_decoded;
batch.seq_id[batch.n_tokens] = client.id;
batch.logits[batch.n_tokens] = true;
client.n_decoded += 1;
client.i_batch = batch.n_tokens;
batch.n_tokens += 1;
}
if (batch.n_tokens == 0) {
// all sequences have ended - clear the entire KV cache
for (int i = 0; i < n_clients; ++i) {
llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
}
LOG_TEE("%s: clearing the KV cache\n", __func__);
}
// insert new sequences for decoding
if (cont_batching || batch.n_tokens == 0) {
for (auto & client : clients) {
if (client.seq_id == -1 && g_seq_id < n_seq) {
client.seq_id = g_seq_id;
client.t_start_prompt = ggml_time_us();
client.t_start_gen = 0;
client.input = k_prompts[rand() % k_prompts.size()];
client.prompt = client.input + "\nAssistant:";
client.response = "";
std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0);
// do not prepend BOS because we have a system prompt!
std::vector<llama_token> tokens_prompt;
tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
for (size_t i = 0; i < tokens_prompt.size(); ++i) {
batch.token [batch.n_tokens] = tokens_prompt[i];
batch.pos [batch.n_tokens] = i + n_tokens_system;
batch.seq_id[batch.n_tokens] = client.id;
batch.logits[batch.n_tokens] = false;
batch.n_tokens += 1;
}
// extract the logits only for the last token
if (batch.n_tokens > 0) {
batch.logits[batch.n_tokens - 1] = true;
}
client.n_prompt = tokens_prompt.size();
client.n_decoded = 0;
client.i_batch = batch.n_tokens - 1;
LOG_TEE("\033[1mClient %3d, seq %4d, started decoding ...\033[0m\n", client.id, client.seq_id);
g_seq_id += 1;
// insert new requests one-by-one
//if (cont_batching) {
// break;
//}
}
}
}
if (batch.n_tokens == 0) {
break;
}
// process in chunks of params.n_batch
int32_t n_batch = params.n_batch;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
// experiment: process in powers of 2
//if (i + n_batch > (int32_t) batch.n_tokens && n_batch > 32) {
// n_batch /= 2;
// i -= n_batch;
// continue;
//}
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view, params.n_threads);
if (ret != 0) {
if (n_batch == 1 || ret < 0) {
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
return 1;
}
LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2);
n_cache_miss += 1;
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
i -= n_batch;
continue;
}
LOG("%s : decoded batch of %d tokens\n", __func__, n_tokens);
for (auto & client : clients) {
if (client.i_batch < (int) i || client.i_batch >= (int) (i + n_tokens)) {
continue;
}
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
const llama_token id = llama_sample_token(ctx, NULL, NULL, params, client.tokens_prev, candidates, client.i_batch - i);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
// have their prompt already processed
client.t_start_gen = ggml_time_us();
}
// remember which tokens were sampled - used for repetition penalties during sampling
client.tokens_prev.erase(client.tokens_prev.begin());
client.tokens_prev.push_back(id);
const std::string token_str = llama_token_to_piece(ctx, id);
client.response += token_str;
client.sampled = id;
//printf("client %d, seq %d, token %d, pos %d, batch %d: %s\n",
// client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
if (client.n_decoded > 2 &&
(id == llama_token_eos(ctx) ||
(params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
client.response.find("User:") != std::string::npos ||
client.response.find('\n') != std::string::npos)) {
// basic reverse prompt
const size_t pos = client.response.find("User:");
if (pos != std::string::npos) {
client.response = client.response.substr(0, pos);
}
// delete only the generated part of the sequence, i.e. keep the system prompt in the cache
llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, n_ctx);
const auto t_main_end = ggml_time_us();
LOG_TEE("\033[1mClient %3d, seq %4d, prompt %4d t, response %4d t, time %5.2f s, speed %5.2f t/s, cache miss %d \033[0m \n\nInput: %s\nResponse: %s\n\n",
client.id, client.seq_id, client.n_prompt, client.n_decoded,
(t_main_end - client.t_start_prompt) / 1e6,
(double) (client.n_prompt + client.n_decoded) / (t_main_end - client.t_start_prompt) * 1e6,
n_cache_miss,
::trim(client.input).c_str(),
::trim(client.response).c_str());
n_total_prompt += client.n_prompt;
n_total_gen += client.n_decoded;
client.seq_id = -1;
}
client.i_batch = -1;
}
}
}
const auto t_main_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("Total prompt tokens: %6d, speed: %5.2f t/s\n", n_total_prompt, (double) (n_total_prompt ) / (t_main_end - t_main_start) * 1e6);
LOG_TEE("Total gen tokens: %6d, speed: %5.2f t/s\n", n_total_gen, (double) (n_total_gen ) / (t_main_end - t_main_start) * 1e6);
LOG_TEE("Total speed (AVG): %6s speed: %5.2f t/s\n", "", (double) (n_total_prompt + n_total_gen) / (t_main_end - t_main_start) * 1e6);
LOG_TEE("Cache misses: %6d\n", n_cache_miss);
LOG_TEE("\n\n");
llama_print_timings(ctx);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

51
examples/perplexity/perplexity.cpp
View File

@ -80,7 +80,9 @@ static void write_logfile(
static std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
for (float v : logits) {
max_logit = std::max(max_logit, v);
}
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
@ -89,15 +91,21 @@ static std::vector<float> softmax(const std::vector<float>& logits) {
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
for (size_t i = 0; i < probs.size(); i++) {
probs[i] /= sum_exp;
}
return probs;
}
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
float max_logit = logits[0];
for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
for (int i = 1; i < n_vocab; ++i) {
max_logit = std::max(max_logit, logits[i]);
}
double sum_exp = 0.0;
for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
for (int i = 0; i < n_vocab; ++i) {
sum_exp += expf(logits[i] - max_logit);
}
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
}
@ -108,7 +116,8 @@ static void process_logits(
std::mutex mutex;
int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
double local_nll = 0, local_nll2 = 0;
double local_nll = 0;
double local_nll2 = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int i = counter++;
@ -126,10 +135,13 @@ static void process_logits(
prob_history[i] = results.prob;
}
};
for (auto & w : workers) w = std::thread(compute);
for (auto & w : workers) {
w = std::thread(compute);
}
compute();
for (auto & w : workers) w.join();
for (auto & w : workers) {
w.join();
}
}
static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
@ -152,8 +164,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
return {std::move(tokens), 0., {}, {}};
}
std::vector<float> logit_history;
std::vector<float> prob_history;
std::vector<float> logit_history;
std::vector<float> prob_history;
logit_history.resize(tokens.size());
prob_history.resize(tokens.size());
@ -195,12 +207,15 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
const auto t_start = std::chrono::high_resolution_clock::now();
// clear the KV cache
llama_kv_cache_tokens_rm(ctx, -1, -1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0), params.n_threads)) {
//fprintf(stderr, "%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
}
@ -320,6 +335,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const auto t_start = std::chrono::high_resolution_clock::now();
// clear the KV cache
llama_kv_cache_tokens_rm(ctx, -1, -1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
@ -332,7 +350,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
tokens[batch_start] = llama_token_bos(ctx);
}
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
}
@ -402,7 +420,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
}
static std::vector<float> hellaswag_evaluate_tokens(
llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch, int n_vocab, int n_thread
llama_context * ctx, std::vector<int> & tokens, int n_past, int n_batch, int n_vocab, int n_thread
) {
std::vector<float> result;
result.reserve(tokens.size() * n_vocab);
@ -410,7 +428,7 @@ static std::vector<float> hellaswag_evaluate_tokens(
for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {
size_t n_tokens = tokens.size() - i_chunk * n_batch;
n_tokens = std::min(n_tokens, size_t(n_batch));
if (llama_eval(ctx, tokens.data() + i_chunk * n_batch, n_tokens, n_past, n_thread)) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + i_chunk * n_batch, n_tokens, n_past, 0), n_thread)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return {};
}
@ -550,6 +568,9 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
query_embd.resize(32);
}
// clear the KV cache
llama_kv_cache_tokens_rm(ctx, -1, -1);
auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab, params.n_threads);
if (logits.empty()) {
fprintf(stderr, "%s : failed to eval\n", __func__);
@ -661,7 +682,7 @@ int main(int argc, char ** argv) {
return 1;
}
params.perplexity = true;
params.logits_all = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
if (params.ppl_stride > 0) {

16
examples/save-load-state/save-load-state.cpp
View File

@ -35,11 +35,11 @@ int main(int argc, char ** argv) {
auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
// init
auto model = llama_load_model_from_file(params.model.c_str(), lparams);
auto * model = llama_load_model_from_file(params.model.c_str(), lparams);
if (model == nullptr) {
return 1;
}
auto ctx = llama_new_context_with_model(model, lparams);
auto * ctx = llama_new_context_with_model(model, lparams);
if (ctx == nullptr) {
llama_free_model(model);
return 1;
@ -54,7 +54,7 @@ int main(int argc, char ** argv) {
}
// evaluate prompt
llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads);
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt_tokens, n_past, 0), params.n_threads);
last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
n_past += n_prompt_tokens;
@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
printf("\n%s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx);
auto * logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -91,7 +91,7 @@ int main(int argc, char ** argv) {
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0), params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
// make new context
auto ctx2 = llama_new_context_with_model(model, lparams);
auto * ctx2 = llama_new_context_with_model(model, lparams);
// Load state (rng, logits, embedding and kv_cache) from file
{
@ -138,7 +138,7 @@ int main(int argc, char ** argv) {
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx2);
auto * logits = llama_get_logits(ctx2);
auto n_vocab = llama_n_vocab(ctx2);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -151,7 +151,7 @@ int main(int argc, char ** argv) {
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0), params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);

35
examples/server/server.cpp
View File

@ -381,6 +381,10 @@ struct llama_server_context
// compare the evaluated prompt with the new prompt
n_past = common_part(embd, prompt_tokens);
// since #3228 we now have to manually manage the KV cache
llama_kv_cache_seq_rm(ctx, 0, n_past, params.n_ctx);
embd = prompt_tokens;
if (n_past == num_prompt_tokens)
{
@ -411,19 +415,27 @@ struct llama_server_context
if (embd.size() >= (size_t)params.n_ctx)
{
// Reset context
const int n_left = (params.n_ctx - params.n_keep) / 2;
// Shift context
const int n_left = n_past - params.n_keep - 1;
const int n_discard = n_left/2;
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
for (size_t i = params.n_keep + 1 + n_discard; i < embd.size(); i++)
{
embd[i - n_discard] = embd[i];
}
embd.resize(embd.size() - n_discard);
n_past -= n_discard;
std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
embd = new_tokens;
n_past = params.n_keep;
truncated = true;
LOG_VERBOSE("input truncated", {
{"n_ctx", params.n_ctx},
{"n_keep", params.n_keep},
{"n_left", n_left},
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
});
}
@ -434,7 +446,8 @@ struct llama_server_context
{
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
if (llama_decode(ctx, llama_batch_get_one(&embd[n_past], n_eval, n_past, 0), params.n_threads))
{
LOG_ERROR("failed to eval", {
{"n_eval", n_eval},
@ -523,13 +536,13 @@ struct llama_server_context
{
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
}
else if (mirostat == 2)
{
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
}
else
@ -540,7 +553,7 @@ struct llama_server_context
llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
llama_sample_typical(ctx, &candidates_p, typical_p, min_keep);
llama_sample_top_p(ctx, &candidates_p, top_p, min_keep);
llama_sample_temperature(ctx, &candidates_p, temp);
llama_sample_temp(ctx, &candidates_p, temp);
result.tok = llama_sample_token(ctx, &candidates_p);
}
}

21
examples/simple/README.md Normal file
View File

@ -0,0 +1,21 @@
# llama.cpp/example/simple
The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt.
```bash
./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is"
...
main: n_len = 32, n_ctx = 2048, n_parallel = 1, n_kv_req = 32
Hello my name is Shawn and I'm a 20 year old male from the United States. I'm a 20 year old
main: decoded 27 tokens in 2.31 s, speed: 11.68 t/s
llama_print_timings: load time = 579.15 ms
llama_print_timings: sample time = 0.72 ms / 28 runs ( 0.03 ms per token, 38888.89 tokens per second)
llama_print_timings: prompt eval time = 655.63 ms / 10 tokens ( 65.56 ms per token, 15.25 tokens per second)
llama_print_timings: eval time = 2180.97 ms / 27 runs ( 80.78 ms per token, 12.38 tokens per second)
llama_print_timings: total time = 2891.13 ms
```

156
examples/simple/simple.cpp
View File

@ -26,12 +26,18 @@ int main(int argc, char ** argv) {
params.prompt = "Hello my name is";
}
// total length of the sequence including the prompt
const int n_len = 32;
// init LLM
llama_backend_init(params.numa);
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = 1234;
ctx_params.n_ctx = 2048;
llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
if (model == NULL) {
@ -41,20 +47,31 @@ int main(int argc, char ** argv) {
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
if ((int) tokens_list.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
return 1;
}
fprintf(stderr, "\n\n");
// print the prompt token-by-token
fprintf(stderr, "\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
@ -62,63 +79,104 @@ int main(int argc, char ** argv) {
fflush(stderr);
// create a llama_batch with size 512
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(512, 0);
// evaluate the initial prompt
batch.n_tokens = tokens_list.size();
for (int32_t i = 0; i < batch.n_tokens; i++) {
batch.token[i] = tokens_list[i];
batch.pos[i] = i;
batch.seq_id[i] = 0;
batch.logits[i] = false;
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx, batch, params.n_threads) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
// main loop
// The LLM keeps a contextual cache memory of previous token evaluation.
// Usually, once this cache is full, it is required to recompute a compressed context based on previous
// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
// example, we will just stop the loop once this cache is full or once an end of stream is detected.
int n_cur = batch.n_tokens;
int n_decode = 0;
const int n_gen = std::min(32, max_context_size);
const auto t_main_start = ggml_time_us();
while (llama_get_kv_cache_token_count(ctx) < n_gen) {
// evaluate the transformer
while (n_cur <= n_len) {
// sample the next token
{
auto n_vocab = llama_n_vocab(ctx);
auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// sample the most likely token
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream?
if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
LOG_TEE("\n");
break;
}
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
// prepare the next batch
batch.n_tokens = 0;
// push this new token for next evaluation
batch.token [batch.n_tokens] = new_token_id;
batch.pos [batch.n_tokens] = n_cur;
batch.seq_id[batch.n_tokens] = 0;
batch.logits[batch.n_tokens] = true;
batch.n_tokens += 1;
n_decode += 1;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
tokens_list.clear();
// sample the next token
llama_token new_token_id = 0;
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
// is it an end of stream ?
if (new_token_id == llama_token_eos(ctx)) {
fprintf(stderr, " [end of text]\n");
break;
}
// print the new token :
printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
// push this new token for next evaluation
tokens_list.push_back(new_token_id);
}
LOG_TEE("\n");
const auto t_main_end = ggml_time_us();
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
llama_print_timings(ctx);
fprintf(stderr, "\n");
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

19
examples/speculative/speculative.cpp
View File

@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL;
// load the target model
params.perplexity = true; // HACK: enable logits_all = true
params.logits_all = true;
std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
// load the draft model
@ -70,9 +70,9 @@ int main(int argc, char ** argv) {
const auto t_enc_start = ggml_time_us();
// eval the prompt with both models
llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads);
llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads);
llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads);
llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0), params.n_threads);
llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0), params.n_threads);
llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0), params.n_threads);
const auto t_enc_end = ggml_time_us();
@ -134,7 +134,7 @@ int main(int argc, char ** argv) {
while (true) {
// sample from the target model
const llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
// remember which tokens were sampled - used for repetition penalties during sampling
last_tokens.erase(last_tokens.begin());
@ -172,7 +172,8 @@ int main(int argc, char ** argv) {
LOG("out of drafted tokens\n");
}
llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, n_ctx);
llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0), params.n_threads);
++n_past_dft;
// heuristic for n_draft
@ -256,7 +257,8 @@ int main(int argc, char ** argv) {
}
// evaluate the drafted token on the draft model
llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_cur, n_ctx);
llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0), params.n_threads);
++n_past_cur;
if (grammar_dft != NULL) {
@ -265,7 +267,8 @@ int main(int argc, char ** argv) {
}
// evaluate the target model on the drafted tokens
llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
llama_kv_cache_seq_rm(ctx_tgt, 0, n_past_tgt, n_ctx);
llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0), params.n_threads);
++n_past_tgt;
// the first token is always proposed by the traget model before the speculation loop

16
examples/train-text-from-scratch/train-text-from-scratch.cpp
View File

@ -679,15 +679,23 @@ struct ggml_tensor * llama_build_train_graphs(
}
};
// KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// rope has so much parameters that we make a custom function for it
auto rope = [ctx, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
(struct ggml_tensor * t) -> struct ggml_tensor * {
// not capturing these, to silcence warnings
const int n_past = 0;
const int rope_mode = 0;
return ggml_rope_custom(ctx,
t, n_past, n_rot, rope_mode, n_ctx,
t, KQ_pos, n_rot, rope_mode, n_ctx,
rope_freq_base, rope_freq_scale);
};
@ -787,6 +795,8 @@ struct ggml_tensor * llama_build_train_graphs(
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
// input gradient
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, one));
GGML_ASSERT(t36->grad->data == NULL && !ggml_is_view(t36->grad));
ggml_allocr_alloc(alloc, t36->grad);
// gradient tensors (will be set to zero by ggml_graph_reset)

147
ggml-cuda.cu
View File

@ -4369,8 +4369,10 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
}
// rope == RoPE == rotary positional embedding
static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale) {
template<typename T, bool has_pos>
static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale) {
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (col >= ncols) {
@ -4379,8 +4381,11 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int i = row*ncols + col;
const int i2 = row/p_delta_rows;
const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
const int p = has_pos ? pos[i2] : 0;
const float p0 = p*freq_scale;
const float theta = p0*powf(theta_scale, col/2);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta);
@ -4391,8 +4396,9 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
dst[i + 1] = x0*sin_theta + x1*cos_theta;
}
static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale) {
template<typename T, bool has_pos>
static __global__ void rope_neox(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale) {
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (col >= ncols) {
@ -4401,8 +4407,11 @@ static __global__ void rope_neox_f32(const float * x, float * dst, const int nco
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int i = row*ncols + col/2;
const int i2 = row/p_delta_rows;
const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
const int p = has_pos ? pos[i2] : 0;
const float p0 = p*freq_scale;
const float theta = p0*powf(theta_scale, col/2);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta);
@ -4413,8 +4422,8 @@ static __global__ void rope_neox_f32(const float * x, float * dst, const int nco
dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
}
static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx) {
static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale, const int n_ctx) {
const int col = blockDim.x*blockIdx.x + threadIdx.x;
const int half_n_dims = ncols/4;
@ -4424,11 +4433,13 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
const int row = blockDim.y*blockIdx.y + threadIdx.y;
const int i = row*ncols + col;
const int i2 = row/p_delta_rows;
const float col_theta_scale = powf(theta_scale, col);
const float p = p0 + p_delta*(row/p_delta_rows);
// FIXME: this is likely wrong
const int p = pos != nullptr ? pos[i2] : 0;
const float theta = min(p, p_delta*(n_ctx - 2))*col_theta_scale;
const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta);
@ -4438,7 +4449,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
const float block_theta = max(p - p_delta*(n_ctx - 2), 0.f)*col_theta_scale;
const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
const float sin_block_theta = sinf(block_theta);
const float cos_block_theta = cosf(block_theta);
@ -5389,31 +5400,41 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
}
static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
template<typename T>
static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
if (pos == nullptr) {
rope<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
} else {
rope<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
}
}
static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
template<typename T>
static void rope_neox_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
if (pos == nullptr) {
rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
} else {
rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
}
}
static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
GGML_ASSERT(ncols % 4 == 0);
const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
const dim3 block_nums(num_blocks_x, nrows, 1);
rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale, n_ctx);
rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx);
}
static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
@ -6136,14 +6157,16 @@ inline void ggml_cuda_op_rope(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t nrows = ggml_nrows(src0);
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_ctx = ((int32_t *) dst->op_params)[3];
@ -6154,19 +6177,38 @@ inline void ggml_cuda_op_rope(
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
const int32_t * pos = nullptr;
if ((mode & 1) == 0) {
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(src1->ne[0] == ne2);
pos = (const int32_t *) src1_dd;
}
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;
// compute
if (is_glm) {
rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, n_ctx, main_stream);
GGML_ASSERT(false);
rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream);
} else if (is_neox) {
GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
rope_neox_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, main_stream);
if (src0->type == GGML_TYPE_F32) {
rope_neox_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
} else if (src0->type == GGML_TYPE_F16) {
rope_neox_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
} else {
GGML_ASSERT(false);
}
} else {
rope_f32_cuda(src0_dd, dst_dd, ne00, nrows, p0, freq_scale, ne01, theta_scale, main_stream);
if (src0->type == GGML_TYPE_F32) {
rope_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
} else if (src0->type == GGML_TYPE_F16) {
rope_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
} else {
GGML_ASSERT(false);
}
}
(void) src1;
@ -6337,7 +6379,7 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
}
}
void ggml_cuda_set_peer_access(const int n_tokens) {
static void ggml_cuda_set_peer_access(const int n_tokens) {
static bool peer_access_enabled = false;
const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
@ -6665,27 +6707,27 @@ static void ggml_cuda_op_mul_mat(
}
}
void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
}
void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul);
}
void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu);
}
void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
}
void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
}
void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
}
@ -6706,7 +6748,7 @@ bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_te
return false;
}
void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
@ -6735,7 +6777,7 @@ void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * sr
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
}
void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
GGML_ASSERT(!ggml_is_contiguous(src0) && ggml_is_contiguous(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
@ -6769,7 +6811,7 @@ void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1
ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
}
void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU;
@ -6813,11 +6855,11 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
}
}
void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
}
void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne = ggml_nelements(src0);
GGML_ASSERT(ne == ggml_nelements(src1));
@ -6865,29 +6907,29 @@ void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
(void) dst;
}
void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_cpy(src0, dst, nullptr);
(void) src1;
}
void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf);
}
void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max);
}
void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope);
}
void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
}
void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
(void) src0;
(void) src1;
(void) dst;
@ -7010,11 +7052,13 @@ static struct ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
return extra;
}
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
if (scratch && g_scratch_size == 0) {
return;
}
tensor->backend = GGML_BACKEND_GPU;
// recursively assign CUDA buffers until a compute tensor is found
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
const ggml_op src0_op = tensor->src[0]->op;
@ -7026,8 +7070,6 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
}
tensor->backend = GGML_BACKEND_GPU;
if (scratch && no_alloc) {
return;
}
@ -7112,6 +7154,15 @@ void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset)
tensor->extra = extra;
}
void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(ggml_is_contiguous(tensor));
struct ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice));
}
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, true, false, false);
}

1
ggml-cuda.h
View File

@ -31,6 +31,7 @@ GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tens
GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
GGML_API void ggml_cuda_copy_to_device(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_set_main_device(int main_device);
GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);

131
ggml-metal.m
View File

@ -103,7 +103,8 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(rope_f32);
GGML_METAL_DECL_KERNEL(rope_f16);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
@ -293,7 +294,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(rope_f32);
GGML_METAL_ADD_KERNEL(rope_f16);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
@ -367,7 +369,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DEL_KERNEL(rope);
GGML_METAL_DEL_KERNEL(rope_f32);
GGML_METAL_DEL_KERNEL(rope_f16);
GGML_METAL_DEL_KERNEL(alibi_f32);
GGML_METAL_DEL_KERNEL(cpy_f32_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f32);
@ -768,25 +771,59 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
// utilize float4
GGML_ASSERT(ne00 % 4 == 0);
const int64_t nb = ne00/4;
bool bcast_row = false;
if (ggml_nelements(src1) == ne10) {
int64_t nb = ne00;
if (ggml_nelements(src1) == ne10 && ne00 % 4 == 0) {
// src1 is a row
GGML_ASSERT(ne11 == 1);
nb = ne00 / 4;
[encoder setComputePipelineState:ctx->pipeline_add_row];
bcast_row = true;
} else {
[encoder setComputePipelineState:ctx->pipeline_add];
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&nb length:sizeof(nb) atIndex:3];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
[encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26];
[encoder setBytes:&nb length:sizeof(nb) atIndex:27];
const int64_t n = ggml_nelements(dst)/4;
if (bcast_row) {
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} else {
const int nth = MIN(1024, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
}
} break;
case GGML_OP_MUL:
{
@ -868,7 +905,7 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_SOFT_MAX:
{
const int nth = 32;
const int nth = MIN(32, ne00);
if (ne00%4 == 0) {
[encoder setComputePipelineState:ctx->pipeline_soft_max_4];
@ -921,7 +958,7 @@ void ggml_metal_graph_compute(
src1t == GGML_TYPE_F32 &&
[ctx->device supportsFamily:MTLGPUFamilyApple7] &&
ne00%32 == 0 &&
ne11 > 1) {
ne11 > 2) {
switch (src0->type) {
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
@ -1132,7 +1169,7 @@ void ggml_metal_graph_compute(
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
const int nth = 512;
const int nth = MIN(512, ne00);
[encoder setComputePipelineState:ctx->pipeline_rms_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@ -1151,7 +1188,7 @@ void ggml_metal_graph_compute(
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
const int nth = 256;
const int nth = MIN(256, ne00);
[encoder setComputePipelineState:ctx->pipeline_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@ -1169,6 +1206,8 @@ void ggml_metal_graph_compute(
{
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int nth = MIN(1024, ne00);
const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
@ -1202,12 +1241,14 @@ void ggml_metal_graph_compute(
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&m0 length:sizeof( float) atIndex:18];
const int nth = 32;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ROPE:
{
GGML_ASSERT(ne10 == ne02);
const int nth = MIN(1024, ne00);
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
@ -1217,38 +1258,44 @@ void ggml_metal_graph_compute(
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
[encoder setComputePipelineState:ctx->pipeline_rope];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&n_past length:sizeof( int) atIndex:18];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
[encoder setBytes:&mode length:sizeof( int) atIndex:20];
[encoder setBytes:&freq_base length:sizeof(float) atIndex:21];
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
switch (src0->type) {
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_rope_f16]; break;
default: GGML_ASSERT(false);
};
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18];
[encoder setBytes:&n_past length:sizeof( int) atIndex:19];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:20];
[encoder setBytes:&mode length:sizeof( int) atIndex:21];
[encoder setBytes:&freq_base length:sizeof(float) atIndex:22];
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:23];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
{
const int nth = 32;
const int nth = MIN(1024, ne00);
switch (src0t) {
case GGML_TYPE_F32:

159
ggml-metal.metal
View File

@ -24,12 +24,59 @@ typedef struct {
int8_t qs[QK8_0]; // quants
} block_q8_0;
// general-purpose kernel for addition of two tensors
// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
// cons: not very efficient
kernel void kernel_add(
device const float4 * src0,
device const float4 * src1,
device float4 * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] + src1[tpig];
device const char * src0,
device const char * src1,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant int64_t & nb00,
constant int64_t & nb01,
constant int64_t & nb02,
constant int64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant int64_t & nb10,
constant int64_t & nb11,
constant int64_t & nb12,
constant int64_t & nb13,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant int64_t & nb0,
constant int64_t & nb1,
constant int64_t & nb2,
constant int64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig.z;
const int64_t i02 = tgpig.y;
const int64_t i01 = tgpig.x;
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00;
device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10;
device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0] + ((device float *)src1_ptr)[0];
src0_ptr += ntg.x*nb00;
src1_ptr += ntg.x*nb10;
dst_ptr += ntg.x*nb0;
}
}
// assumption: src1 is a row
@ -38,7 +85,7 @@ kernel void kernel_add_row(
device const float4 * src0,
device const float4 * src1,
device float4 * dst,
constant int64_t & nb,
constant int64_t & nb [[buffer(27)]],
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] + src1[tpig % nb];
}
@ -806,30 +853,61 @@ kernel void kernel_alibi_f32(
}
}
typedef void (rope_t)(
device const void * src0,
device const int32_t * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int & n_past,
constant int & n_dims,
constant int & mode,
constant float & freq_base,
constant float & freq_scale,
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg[[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]);
template<typename T>
kernel void kernel_rope(
device const void * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int & n_past,
constant int & n_dims,
constant int & mode,
constant float & freq_base,
constant float & freq_scale,
device const void * src0,
device const int32_t * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int & n_past,
constant int & n_dims,
constant int & mode,
constant float & freq_base,
constant float & freq_scale,
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg[[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
@ -839,7 +917,9 @@ kernel void kernel_rope(
const bool is_neox = mode & 2;
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
device const int32_t * pos = src1;
const int64_t p = pos[i2];
const float theta_0 = freq_scale * (float)p;
const float inv_ndims = -1.f/n_dims;
@ -851,11 +931,11 @@ kernel void kernel_rope(
const float cos_theta = cos(theta);
const float sin_theta = sin(theta);
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const float x0 = src[0];
const float x1 = src[1];
const T x0 = src[0];
const T x1 = src[1];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
@ -870,8 +950,8 @@ kernel void kernel_rope(
const int64_t i0 = ib*n_dims + ic/2;
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const float x0 = src[0];
const float x1 = src[n_dims/2];
@ -883,6 +963,9 @@ kernel void kernel_rope(
}
}
template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope<float>;
template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope<half>;
kernel void kernel_cpy_f16_f16(
device const half * src0,
device half * dst,
@ -1273,8 +1356,8 @@ kernel void kernel_mul_mat_q3_K_f32(
float yl[32];
const uint16_t kmask1 = 0x3030;
const uint16_t kmask2 = 0x0f0f;
//const uint16_t kmask1 = 0x3030;
//const uint16_t kmask2 = 0x0f0f;
const int tid = tiisg/4;
const int ix = tiisg%4;

170
ggml.c
View File

@ -6406,6 +6406,54 @@ struct ggml_tensor * ggml_cont_inplace(
return ggml_cont_impl(ctx, a, true);
}
// make contiguous, with new shape
GGML_API struct ggml_tensor * ggml_cont_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0) {
return ggml_cont_4d(ctx, a, ne0, 1, 1, 1);
}
GGML_API struct ggml_tensor * ggml_cont_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1) {
return ggml_cont_4d(ctx, a, ne0, ne1, 1, 1);
}
GGML_API struct ggml_tensor * ggml_cont_3d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2) {
return ggml_cont_4d(ctx, a, ne0, ne1, ne2, 1);
}
struct ggml_tensor * ggml_cont_4d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3) {
GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3));
bool is_node = false;
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
ggml_format_name(result, "%s (cont)", a->name);
result->op = GGML_OP_CONT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
// ggml_reshape
struct ggml_tensor * ggml_reshape(
@ -6968,7 +7016,7 @@ struct ggml_tensor * ggml_soft_max_back_inplace(
static struct ggml_tensor * ggml_rope_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
@ -6977,7 +7025,10 @@ static struct ggml_tensor * ggml_rope_impl(
float xpos_base,
bool xpos_down,
bool inplace) {
GGML_ASSERT(n_past >= 0);
GGML_ASSERT(ggml_is_vector(b));
GGML_ASSERT(b->type == GGML_TYPE_I32);
GGML_ASSERT(a->ne[2] == b->ne[0]);
bool is_node = false;
if (a->grad) {
@ -6986,7 +7037,7 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[8] = { n_past, n_dims, mode, n_ctx };
int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
memcpy(params + 4, &freq_base, sizeof(float));
memcpy(params + 5, &freq_scale, sizeof(float));
memcpy(params + 6, &xpos_base, sizeof(float));
@ -6996,6 +7047,7 @@ static struct ggml_tensor * ggml_rope_impl(
result->op = GGML_OP_ROPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
return result;
}
@ -7003,55 +7055,55 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
}
struct ggml_tensor * ggml_rope_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
}
struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
}
struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
}
struct ggml_tensor * ggml_rope_xpos_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
float base,
bool down) {
return ggml_rope_impl(ctx, a, n_past, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
}
// ggml_rope_back
@ -7059,7 +7111,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace(
struct ggml_tensor * ggml_rope_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
@ -7067,7 +7119,10 @@ struct ggml_tensor * ggml_rope_back(
float freq_scale,
float xpos_base,
bool xpos_down) {
GGML_ASSERT(n_past >= 0);
GGML_ASSERT(ggml_is_vector(b));
GGML_ASSERT(b->type == GGML_TYPE_I32);
GGML_ASSERT(a->ne[2] == b->ne[0]);
GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
bool is_node = false;
@ -7078,7 +7133,7 @@ struct ggml_tensor * ggml_rope_back(
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
int32_t params[8] = { n_past, n_dims, mode, n_ctx };
int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
memcpy(params + 4, &freq_base, sizeof(float));
memcpy(params + 5, &freq_scale, sizeof(float));
memcpy(params + 6, &xpos_base, sizeof(float));
@ -7088,6 +7143,7 @@ struct ggml_tensor * ggml_rope_back(
result->op = GGML_OP_ROPE_BACK;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
return result;
}
@ -8798,8 +8854,6 @@ static void ggml_compute_forward_add_f32(
#else
ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
// }
// }
}
} else {
// src1 is not contiguous
@ -12456,13 +12510,11 @@ static void ggml_compute_forward_alibi_f16(
return;
}
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
assert(n_past >= 0);
const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
const int ne1 = src0->ne[1]; // seq_len_without_past
const int ne2 = src0->ne[2]; // n_head -> this is k
@ -12477,7 +12529,7 @@ static void ggml_compute_forward_alibi_f16(
//const int nb3 = src0->nb[3];
GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
//GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
GGML_ASSERT(n_head == ne2);
// add alibi to src0 (KQ_scaled)
@ -12623,8 +12675,8 @@ static void ggml_compute_forward_clamp(
static void ggml_compute_forward_rope_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
@ -12634,9 +12686,9 @@ static void ggml_compute_forward_rope_f32(
// these two only relevant for xPos RoPE:
float xpos_base;
bool xpos_down;
bool xpos_down;
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_ctx = ((int32_t *) dst->op_params)[3];
@ -12645,8 +12697,6 @@ static void ggml_compute_forward_rope_f32(
memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool));
assert(n_past >= 0);
GGML_TENSOR_UNARY_OP_LOCALS;
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
@ -12677,9 +12727,11 @@ static void ggml_compute_forward_rope_f32(
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;
const int32_t * pos = (const int32_t *) src1->data;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i2 = 0; i2 < ne2; i2++) {
const int64_t p = pos[i2];
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -12716,7 +12768,7 @@ static void ggml_compute_forward_rope_f32(
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
// zeta scaling for xPos only:
float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
if (xpos_down) zeta = 1.0f / zeta;
theta *= theta_scale;
@ -12761,8 +12813,8 @@ static void ggml_compute_forward_rope_f32(
static void ggml_compute_forward_rope_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
@ -12770,15 +12822,13 @@ static void ggml_compute_forward_rope_f16(
float freq_base;
float freq_scale;
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_ctx = ((int32_t *) dst->op_params)[3];
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
assert(n_past >= 0);
GGML_TENSOR_UNARY_OP_LOCALS;
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
@ -12809,9 +12859,11 @@ static void ggml_compute_forward_rope_f16(
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;
const int32_t * pos = (const int32_t *) src1->data;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i2 = 0; i2 < ne2; i2++) {
const int64_t p = pos[i2];
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -12890,15 +12942,16 @@ static void ggml_compute_forward_rope_f16(
static void ggml_compute_forward_rope(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F16:
{
ggml_compute_forward_rope_f16(params, src0, dst);
ggml_compute_forward_rope_f16(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_rope_f32(params, src0, dst);
ggml_compute_forward_rope_f32(params, src0, src1, dst);
} break;
default:
{
@ -12912,6 +12965,7 @@ static void ggml_compute_forward_rope(
static void ggml_compute_forward_rope_back_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
@ -12929,7 +12983,7 @@ static void ggml_compute_forward_rope_back_f32(
float xpos_base;
bool xpos_down;
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx);
@ -12938,8 +12992,6 @@ static void ggml_compute_forward_rope_back_f32(
memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool));
assert(n_past >= 0);
GGML_TENSOR_UNARY_OP_LOCALS;
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
@ -12966,9 +13018,11 @@ static void ggml_compute_forward_rope_back_f32(
const bool is_neox = mode & 2;
const int32_t * pos = (const int32_t *) src1->data;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i2 = 0; i2 < ne2; i2++) {
const int64_t p = pos[i2];
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -12980,7 +13034,7 @@ static void ggml_compute_forward_rope_back_f32(
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
// zeta scaling for xPos only:
float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
if (xpos_down) zeta = 1.0f / zeta;
theta *= theta_scale;
@ -13023,6 +13077,7 @@ static void ggml_compute_forward_rope_back_f32(
static void ggml_compute_forward_rope_back_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
@ -13033,12 +13088,10 @@ static void ggml_compute_forward_rope_back_f16(
// dx = rope_back(dy, src1)
// src0 is dy, src1 contains options
const int n_past = ((int32_t *) dst->op_params)[0];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
assert(n_past >= 0);
GGML_TENSOR_UNARY_OP_LOCALS;
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
@ -13065,9 +13118,11 @@ static void ggml_compute_forward_rope_back_f16(
const bool is_neox = mode & 2;
const int32_t * pos = (const int32_t *) src1->data;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i2 = 0; i2 < ne2; i2++) {
const int64_t p = pos[i2];
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -13119,15 +13174,16 @@ static void ggml_compute_forward_rope_back_f16(
static void ggml_compute_forward_rope_back(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F16:
{
ggml_compute_forward_rope_back_f16(params, src0, dst);
ggml_compute_forward_rope_back_f16(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_rope_back_f32(params, src0, dst);
ggml_compute_forward_rope_back_f32(params, src0, src1, dst);
} break;
default:
{
@ -15864,11 +15920,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
} break;
case GGML_OP_ROPE:
{
ggml_compute_forward_rope(params, tensor->src[0], tensor);
ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_ROPE_BACK:
{
ggml_compute_forward_rope_back(params, tensor->src[0], tensor);
ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_ALIBI:
{
@ -16506,7 +16562,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
// necessary for llama
if (src0->grad) {
const int n_past = ((int32_t *) tensor->op_params)[0];
//const int n_past = ((int32_t *) tensor->op_params)[0];
const int n_dims = ((int32_t *) tensor->op_params)[1];
const int mode = ((int32_t *) tensor->op_params)[2];
const int n_ctx = ((int32_t *) tensor->op_params)[3];
@ -16523,7 +16579,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad,
ggml_rope_back(ctx,
tensor->grad,
n_past,
src1,
n_dims,
mode,
n_ctx,
@ -16537,7 +16593,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
case GGML_OP_ROPE_BACK:
{
if (src0->grad) {
const int n_past = ((int32_t *) tensor->op_params)[0];
//const int n_past = ((int32_t *) tensor->op_params)[0];
const int n_dims = ((int32_t *) tensor->op_params)[1];
const int mode = ((int32_t *) tensor->op_params)[2];
const int n_ctx = ((int32_t *) tensor->op_params)[3];
@ -16554,7 +16610,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad,
ggml_rope_impl(ctx,
tensor->grad,
n_past,
src1,
n_dims,
mode,
n_ctx,

45
ggml.h
View File

@ -1055,7 +1055,6 @@ extern "C" {
size_t nb1,
size_t offset);
// a -> b, return view(b)
GGML_API struct ggml_tensor * ggml_cpy(
struct ggml_context * ctx,
@ -1078,6 +1077,33 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// make contiguous, with new shape
GGML_API struct ggml_tensor * ggml_cont_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0);
GGML_API struct ggml_tensor * ggml_cont_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1);
GGML_API struct ggml_tensor * ggml_cont_3d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2);
GGML_API struct ggml_tensor * ggml_cont_4d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape(
@ -1225,14 +1251,15 @@ extern "C" {
struct ggml_tensor * b);
// rotary position embedding
// if mode & 1 == 1, skip n_past elements
// if mode & 1 == 1, skip n_past elements (DEPRECATED)
// if mode & 2 == 1, GPT-NeoX style
// if mode & 4 == 1, ChatGLM style
// TODO: avoid creating a new tensor every time
//
// b is an int32 vector with size a->ne[2], it contains the positions
GGML_API struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx);
@ -1241,7 +1268,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rope_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx);
@ -1250,7 +1277,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
@ -1261,7 +1288,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,
@ -1272,7 +1299,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
float base,
bool down);
@ -1282,7 +1309,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_rope_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
struct ggml_tensor * b,
int n_dims,
int mode,
int n_ctx,

994
llama.cpp
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File diff suppressed because it is too large Load Diff

327
llama.h
View File

@ -37,6 +37,8 @@
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#define LLAMA_MAX_RNG_STATE (64*1024)
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
@ -60,7 +62,9 @@ extern "C" {
struct llama_model;
struct llama_context;
typedef int llama_token;
typedef int32_t llama_pos;
typedef int32_t llama_token;
typedef int32_t llama_seq_id;
enum llama_vocab_type {
LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
@ -80,24 +84,24 @@ extern "C" {
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
@ -116,6 +120,35 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
// Input data for llama_decode
// A llama_batch object can contain input about one or many sequences
// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
//
// - token : the token ids of the input (used when embd is NULL)
// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
// - pos : the positions of the respective token in the sequence
// - seq_id : the sequence to which the respective token belongs
// - logits : if zero, the logits for the respective token will not be output
//
typedef struct llama_batch {
int32_t n_tokens;
llama_token * token;
float * embd;
llama_pos * pos;
llama_seq_id * seq_id;
int8_t * logits;
// NOTE: helpers for smooth API transition - can be deprecated in the future
// for future-proof code, use the above fields instead and ignore everything below
//
// pos[i] = all_pos_0 + i*all_pos_1
//
llama_pos all_pos_0; // used if pos == NULL
llama_pos all_pos_1; // used if pos == NULL
llama_seq_id all_seq_id; // used if seq_id == NULL
} llama_batch;
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
@ -202,6 +235,7 @@ extern "C" {
int32_t n_eval;
};
// Helpers for getting default parameters
LLAMA_API struct llama_context_params llama_context_default_params(void);
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
@ -246,8 +280,10 @@ extern "C" {
// Get a string describing the model type
LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
// Returns the total size of all the tensors in the model in bytes
LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
// Returns the total number of parameters in the model
LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
@ -268,7 +304,7 @@ extern "C" {
const char * path_lora,
const char * path_base_model,
int n_threads),
"please use llama_model_apply_lora_from_file instead");
"use llama_model_apply_lora_from_file instead");
LLAMA_API int llama_model_apply_lora_from_file(
const struct llama_model * model,
@ -276,11 +312,53 @@ extern "C" {
const char * path_base_model,
int n_threads);
// Returns the number of tokens in the KV cache
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
//
// KV cache
//
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
// Returns the number of tokens in the KV cache
LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
"avoid using this, it will be removed in the future, instead - count the tokens in user code");
// Remove all tokens data of cells in [c0, c1)
LLAMA_API void llama_kv_cache_tokens_rm(
struct llama_context * ctx,
int32_t c0,
int32_t c1);
// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
LLAMA_API void llama_kv_cache_seq_rm(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1);
// Copy all tokens that belong to the specified sequence to another sequence
// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
LLAMA_API void llama_kv_cache_seq_cp(
struct llama_context * ctx,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1);
// Removes all tokens that do not belong to the specified sequence
LLAMA_API void llama_kv_cache_seq_keep(
struct llama_context * ctx,
llama_seq_id seq_id);
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly
LLAMA_API void llama_kv_cache_seq_shift(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta);
//
// State / sessions
//
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens
@ -289,48 +367,100 @@ extern "C" {
// Copies the state to the specified destination address.
// Destination needs to have allocated enough memory.
// Returns the number of bytes copied
LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
LLAMA_API size_t llama_copy_state_data(
struct llama_context * ctx,
uint8_t * dst);
// Set the state reading from the specified address
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
LLAMA_API size_t llama_set_state_data(
struct llama_context * ctx,
uint8_t * src);
// Save/load session file
LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
LLAMA_API bool llama_load_session_file(
struct llama_context * ctx,
const char * path_session,
llama_token * tokens_out,
size_t n_token_capacity,
size_t * n_token_count_out);
// Run the llama inference to obtain the logits and probabilities for the next token.
LLAMA_API bool llama_save_session_file(
struct llama_context * ctx,
const char * path_session,
const llama_token * tokens,
size_t n_token_count);
//
// Decoding
//
// Run the llama inference to obtain the logits and probabilities for the next token(s).
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls
// Returns 0 on success
LLAMA_API int llama_eval(
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
int n_tokens,
llama_token * tokens,
int32_t n_tokens,
int n_past,
int n_threads);
int n_threads),
"use llama_decode() instead");
// Same as llama_eval, but use float matrix input directly.
LLAMA_API int llama_eval_embd(
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
float * embd,
int32_t n_tokens,
int n_past,
int n_threads);
int n_threads),
"use llama_decode() instead");
// Export a static computation graph for context of 511 and batch size of 1
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
// parameters here to keep things simple
// IMPORTANT: do not use for anything else other than debugging and testing!
LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
// Return batch for single sequence of tokens starting at pos_0
//
// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
//
LLAMA_API struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens,
llama_pos pos_0,
llama_seq_id seq_id);
// Allocates a batch of tokens on the heap
// The batch has to be freed with llama_batch_free()
// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
// The rest of the llama_batch members are allocated with size n_tokens
// All members are left uninitialized
LLAMA_API struct llama_batch llama_batch_init(
int32_t n_tokens,
int32_t embd);
// Frees a batch of tokens allocated with llama_batch_init()
LLAMA_API void llama_batch_free(struct llama_batch batch);
// Positive return values does not mean a fatal error, but rather a warning.
// 0 - success
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
// < 0 - error
LLAMA_API int llama_decode(
struct llama_context * ctx,
struct llama_batch batch,
int n_threads);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Can be mutated in order to change the probabilities of the next token
// Rows: n_tokens
// Logits for which llama_batch.logits[i] == 0 are undefined
// Rows: n_tokens provided with llama_batch
// Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
// Logits for the ith token. Equivalent to:
// llama_get_logits(ctx) + i*n_vocab
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for the input
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
@ -407,11 +537,25 @@ extern "C" {
// Sampling functions
//
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
LLAMA_API void llama_sample_repetition_penalty(
struct llama_context * ctx,
llama_token_data_array * candidates,
const llama_token * last_tokens,
size_t last_tokens_size,
float penalty);
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
LLAMA_API void llama_sample_frequency_and_presence_penalties(
struct llama_context * ctx,
llama_token_data_array * candidates,
const llama_token * last_tokens,
size_t last_tokens_size,
float alpha_frequency,
float alpha_presence);
/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
@ -424,23 +568,54 @@ extern "C" {
float scale);
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
LLAMA_API void llama_sample_softmax(
struct llama_context * ctx,
llama_token_data_array * candidates);
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
LLAMA_API void llama_sample_top_k(
struct llama_context * ctx,
llama_token_data_array * candidates,
int k,
size_t min_keep);
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
LLAMA_API void llama_sample_top_p(
struct llama_context * ctx,
llama_token_data_array * candidates,
float p,
size_t min_keep);
/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
LLAMA_API void llama_sample_tail_free(
struct llama_context * ctx,
llama_token_data_array * candidates,
float z,
size_t min_keep);
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
LLAMA_API void llama_sample_typical(
struct llama_context * ctx,
llama_token_data_array * candidates,
float p,
size_t min_keep);
LLAMA_API void llama_sample_temp(
struct llama_context * ctx,
llama_token_data_array * candidates,
float temp);
LLAMA_API DEPRECATED(void llama_sample_temperature(
struct llama_context * ctx,
llama_token_data_array * candidates,
float temp),
"use llama_sample_temp instead");
/// @details Apply constraints from grammar
LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
LLAMA_API void llama_sample_grammar(
struct llama_context * ctx,
llama_token_data_array * candidates,
const struct llama_grammar * grammar);
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
@ -448,23 +623,41 @@ extern "C" {
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
LLAMA_API llama_token llama_sample_token_mirostat(
struct llama_context * ctx,
llama_token_data_array * candidates,
float tau,
float eta,
int m,
float * mu);
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
LLAMA_API llama_token llama_sample_token_mirostat_v2(
struct llama_context * ctx,
llama_token_data_array * candidates,
float tau,
float eta,
float * mu);
/// @details Selects the token with the highest probability.
LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
LLAMA_API llama_token llama_sample_token_greedy(
struct llama_context * ctx,
llama_token_data_array * candidates);
/// @details Randomly selects a token from the candidates based on their probabilities.
LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
LLAMA_API llama_token llama_sample_token(
struct llama_context * ctx,
llama_token_data_array * candidates);
/// @details Accepts the sampled token into the grammar
LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
LLAMA_API void llama_grammar_accept_token(
struct llama_context * ctx,
struct llama_grammar * grammar,
llama_token token);
//
// Beam search
@ -472,9 +665,10 @@ extern "C" {
struct llama_beam_view {
const llama_token * tokens;
size_t n_tokens;
float p; // Cumulative beam probability (renormalized relative to all beams)
bool eob; // Callback should set this to true when a beam is at end-of-beam.
float p; // Cumulative beam probability (renormalized relative to all beams)
bool eob; // Callback should set this to true when a beam is at end-of-beam.
};
// Passed to beam_search_callback function.
@ -483,9 +677,10 @@ extern "C" {
// These pointers are valid only during the synchronous callback, so should not be saved.
struct llama_beams_state {
struct llama_beam_view * beam_views;
size_t n_beams; // Number of elements in beam_views[].
size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
bool last_call; // True iff this is the last callback invocation.
bool last_call; // True iff this is the last callback invocation.
};
// Type of pointer to the beam_search_callback function.
@ -501,10 +696,18 @@ extern "C" {
/// @param n_past Number of tokens already evaluated.
/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
/// @param n_threads Number of threads as passed to llama_eval().
LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
LLAMA_API void llama_beam_search(
struct llama_context * ctx,
llama_beam_search_callback_fn_t callback,
void * callback_data,
size_t n_beams,
int n_past,
int n_predict,
int n_threads);
// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
LLAMA_API void llama_print_timings(struct llama_context * ctx);
LLAMA_API void llama_reset_timings(struct llama_context * ctx);

2
tests/CMakeLists.txt
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@ -37,6 +37,8 @@ llama_build_and_test_executable(test-llama-grammar.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
# llama_build_and_test_executable(test-opt.cpp) # SLOW
llama_build_and_test_executable(test-rope.cpp)
# dummy executable - not installed
get_filename_component(TEST_TARGET test-c.c NAME_WE)
add_executable(${TEST_TARGET} test-c.c)

14
tests/test-grad0.cpp
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@ -1404,6 +1404,11 @@ int main(int argc, const char ** argv) {
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]);
for (int i = 0; i < ne2[2]; ++i) {
((int32_t *) p->data)[i] = n_past + i;
}
ggml_set_param(ctx0, x[0]);
const bool skip_past = (mode & 1);
@ -1415,7 +1420,7 @@ int main(int argc, const char ** argv) {
continue;
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
@ -1438,6 +1443,11 @@ int main(int argc, const char ** argv) {
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f);
struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]);
for (int i = 0; i < ne2[2]; ++i) {
((int32_t *) p->data)[i] = n_past + i;
}
ggml_set_param(ctx0, x[0]);
const bool skip_past = (mode & 1);
@ -1449,7 +1459,7 @@ int main(int argc, const char ** argv) {
continue;
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);

221
tests/test-rope.cpp Normal file
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@ -0,0 +1,221 @@
#include "ggml.h"
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdouble-promotion"
#endif
#define MAX_NARGS 3
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define GGML_SILU_FP16
//
// logging
//
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
static float frand(void) {
return (float)rand()/(float)RAND_MAX;
}
static int irand(int n) {
if (n == 0) return 0;
return rand()%n;
}
static void get_random_dims(int64_t * dims, int ndims) {
dims[0] = dims[1] = dims[2] = dims[3] = 1;
for (int i = 0; i < ndims; i++) {
dims[i] = 1 + irand(4);
}
}
static struct ggml_tensor * get_random_tensor_f32(
struct ggml_context * ctx0,
int ndims,
const int64_t ne[],
float fmin,
float fmax) {
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
switch (ndims) {
case 1:
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
}
break;
case 2:
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
break;
case 3:
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
break;
case 4:
for (int i3 = 0; i3 < ne[3]; i3++) {
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
}
break;
default:
assert(false);
};
return result;
}
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
int main(int /*argc*/, const char ** /*argv*/) {
struct ggml_init_params params = {
/* .mem_size = */ 128*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
std::vector<uint8_t> work_buffer;
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor * x;
// rope f32
for (int m = 0; m < 3; ++m) {
const int ndims = 4;
const int64_t n_rot = 128;
const int64_t ne[4] = { 2*n_rot, 32, 73, 1 };
const int n_past_0 = 100;
const int n_past_2 = 33;
struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
for (int i = 0; i < ne[2]; ++i) {
((int32_t *) p0->data)[i] = n_past_0 + i;
((int32_t *) p1->data)[i] = n_past_2 - n_past_0;
((int32_t *) p2->data)[i] = n_past_2 + i;
}
// test mode 0, 2, 4 (standard, GPT-NeoX, GLM)
const int mode = m == 0 ? 0 : m == 1 ? 2 : 4;
x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
// 100, 101, 102, ..., 172
struct ggml_tensor * r0 = ggml_rope(ctx0, x, p0, n_rot, mode, 1024);
// -67, -67, -67, ..., -67
struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode, 1024); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
// 33, 34, 35, ..., 105
struct ggml_tensor * r2 = ggml_rope(ctx0, x, p2, n_rot, mode, 1024);
ggml_cgraph * gf = ggml_new_graph(ctx0);
ggml_build_forward_expand(gf, r0);
ggml_build_forward_expand(gf, r1);
ggml_build_forward_expand(gf, r2);
ggml_graph_compute_helper(work_buffer, gf, 4);
// check that r1 and r2 are the same
{
double sum0 = 0.0f;
double sum1 = 0.0f;
double diff = 0.0f;
const float * r1_data = (float *) r1->data;
const float * r2_data = (float *) r2->data;
const int n_elements = ggml_nelements(r1);
for (int i = 0; i < n_elements; ++i) {
sum0 += fabs(r1_data[i]);
sum1 += fabs(r2_data[i]);
diff += fabs(r1_data[i] - r2_data[i]);
//if (fabs(r1_data[i] - r2_data[i]) > 0.0001f) {
// printf("%d: %f %f\n", i, r1_data[i], r2_data[i]);
// printf("diff: %f\n", fabs(r1_data[i] - r2_data[i]));
//}
}
//for (int i = 4096; i < 4096 + 128; ++i) {
// printf("%f %f\n", r1_data[i], r2_data[i]);
//}
printf("mode: %d\n", mode);
printf("sum0: %f\n", sum0);
printf("sum1: %f\n", sum1);
printf("diff: %f\n", diff);
printf("rel err: %f\n", diff / sum0);
printf("rel err: %f\n", diff / sum1);
GGML_ASSERT(diff / sum0 < 0.0001f);
GGML_ASSERT(diff / sum1 < 0.0001f);
}
}
ggml_free(ctx0);
return 0;
}