vbatts/llama.cpp
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Eve 2024-12-18 21:38:13 -05:00
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8
.github/workflows/build.yml vendored
View file

@ -317,7 +317,7 @@ jobs:
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
sudo apt-get update -y
sudo apt-get install -y build-essential vulkan-sdk
sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk
- name: Build
id: cmake_build
@ -327,6 +327,12 @@ jobs:
cmake -DGGML_VULKAN=ON ..
cmake --build . --config Release -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
ubuntu-22-cmake-hip:
runs-on: ubuntu-22.04
container: rocm/dev-ubuntu-22.04:6.0.2

4
README.md
View file

@ -221,7 +221,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
| [CUDA](docs/build.md#cuda) | Nvidia GPU |
| [hipBLAS](docs/build.md#hipblas) | AMD GPU |
| [HIP](docs/build.md#hip) | AMD GPU |
| [Vulkan](docs/build.md#vulkan) | GPU |
| [CANN](docs/build.md#cann) | Ascend NPU |
@ -414,7 +414,7 @@ To learn more about model quantization, [read this documentation](examples/quant
[^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
## [`llama-bench`](example/bench)
## [`llama-bench`](examples/llama-bench)
#### Benchmark the performance of the inference for various parameters.

73
common/arg.cpp
View file

@ -119,29 +119,33 @@ std::string common_arg::to_string() {
// utils
//
static void common_params_handle_model_default(common_params & params) {
if (!params.hf_repo.empty()) {
static void common_params_handle_model_default(
std::string & model,
std::string & model_url,
std::string & hf_repo,
std::string & hf_file) {
if (!hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (params.hf_file.empty()) {
if (params.model.empty()) {
if (hf_file.empty()) {
if (model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
params.hf_file = params.model;
} else if (params.model.empty()) {
hf_file = model;
} else if (model.empty()) {
// this is to avoid different repo having same file name, or same file name in different subdirs
std::string filename = params.hf_repo + "_" + params.hf_file;
std::string filename = hf_repo + "_" + hf_file;
// to make sure we don't have any slashes in the filename
string_replace_all(filename, "/", "_");
params.model = fs_get_cache_file(filename);
model = fs_get_cache_file(filename);
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split<std::string>(params.model_url, '#').front();
} else if (!model_url.empty()) {
if (model.empty()) {
auto f = string_split<std::string>(model_url, '#').front();
f = string_split<std::string>(f, '?').front();
params.model = fs_get_cache_file(string_split<std::string>(f, '/').back());
model = fs_get_cache_file(string_split<std::string>(f, '/').back());
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
} else if (model.empty()) {
model = DEFAULT_MODEL_PATH;
}
}
@ -276,7 +280,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
common_params_handle_model_default(params);
// TODO: refactor model params in a common struct
common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file);
common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file);
if (params.escape) {
string_process_escapes(params.prompt);
@ -842,7 +848,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_sparam());
add_opt(common_arg(
{"--sampling-seq"}, "SEQUENCE",
{"--sampling-seq", "--sampler-seq"}, "SEQUENCE",
string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](common_params & params, const std::string & value) {
params.sampling.samplers = common_sampler_types_from_chars(value);
@ -855,13 +861,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.sampling.ignore_eos = true;
}
).set_sparam());
add_opt(common_arg(
{"--penalize-nl"},
string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
[](common_params & params) {
params.sampling.penalize_nl = true;
}
).set_sparam());
add_opt(common_arg(
{"--temp"}, "N",
string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@ -916,6 +915,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--repeat-last-n"}, "N",
string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
[](common_params & params, int value) {
if (value < -1) {
throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
}
params.sampling.penalty_last_n = value;
params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
}
@ -970,6 +972,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--dry-penalty-last-n"}, "N",
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
[](common_params & params, int value) {
if (value < -1) {
throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
}
params.sampling.dry_penalty_last_n = value;
}
).set_sparam());
@ -1582,6 +1587,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.hf_file = value;
}
).set_env("LLAMA_ARG_HF_FILE"));
add_opt(common_arg(
{"-hfrv", "--hf-repo-v"}, "REPO",
"Hugging Face model repository for the vocoder model (default: unused)",
[](common_params & params, const std::string & value) {
params.vocoder.hf_repo = value;
}
).set_env("LLAMA_ARG_HF_REPO_V"));
add_opt(common_arg(
{"-hffv", "--hf-file-v"}, "FILE",
"Hugging Face model file for the vocoder model (default: unused)",
[](common_params & params, const std::string & value) {
params.vocoder.hf_file = value;
}
).set_env("LLAMA_ARG_HF_FILE_V"));
add_opt(common_arg(
{"-hft", "--hf-token"}, "TOKEN",
"Hugging Face access token (default: value from HF_TOKEN environment variable)",
@ -2179,5 +2198,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
add_opt(common_arg(
{"-mv", "--model-vocoder"}, "FNAME",
"vocoder model for audio generation (default: unused)",
[](common_params & params, const std::string & value) {
params.vocoder.model = value;
}
).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
return ctx_arg;
}

30
common/common.cpp
View file

@ -940,6 +940,25 @@ struct common_init_result common_init_from_params(common_params & params) {
params.sampling.ignore_eos = false;
}
if (params.sampling.ignore_eos) {
for (llama_token i = 0; i < llama_n_vocab(model); i++) {
if (llama_token_is_eog(model, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
params.sampling.logit_bias.push_back({i, -INFINITY});
}
}
}
if (params.sampling.penalty_last_n == -1) {
LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.penalty_last_n = llama_n_ctx(lctx);
}
if (params.sampling.dry_penalty_last_n == -1) {
LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
}
if (params.warmup) {
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
@ -1076,7 +1095,7 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
#define CURL_MAX_RETRY 3
#define CURL_RETRY_DELAY_SECONDS 2
static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
int remaining_attempts = max_attempts;
while (remaining_attempts > 0) {
@ -1100,7 +1119,6 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
}
static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
if (!curl) {
@ -1173,11 +1191,13 @@ static bool common_download_file(const std::string & url, const std::string & pa
std::string etag;
std::string last_modified;
};
common_load_model_from_url_headers headers;
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
@ -1761,7 +1781,9 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm)
break;
case 0: // max absolute
for (int i = 0; i < n; i++) {
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
if (sum < std::abs(inp[i])) {
sum = std::abs(inp[i]);
}
}
sum /= 32760.0; // make an int16 range
break;

31
common/common.h
View file

@ -80,6 +80,7 @@ enum llama_example {
LLAMA_EXAMPLE_LLAVA,
LLAMA_EXAMPLE_LOOKUP,
LLAMA_EXAMPLE_PARALLEL,
LLAMA_EXAMPLE_TTS,
LLAMA_EXAMPLE_COUNT,
};
@ -95,6 +96,7 @@ enum common_sampler_type {
COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
COMMON_SAMPLER_TYPE_XTC = 8,
COMMON_SAMPLER_TYPE_INFILL = 9,
COMMON_SAMPLER_TYPE_PENALTIES = 10,
};
// dimensionality reduction methods, used by cvector-generator
@ -130,7 +132,6 @@ struct common_params_sampling {
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
bool timing_per_token = false;
@ -139,6 +140,7 @@ struct common_params_sampling {
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_PENALTIES,
COMMON_SAMPLER_TYPE_DRY,
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TYPICAL_P,
@ -158,6 +160,7 @@ struct common_params_sampling {
struct common_params_speculative {
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_ctx = 0; // draft context size
int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
int32_t n_min = 5; // minimum number of draft tokens to use for speculative decoding
@ -171,6 +174,14 @@ struct common_params_speculative {
std::string model = ""; // draft model for speculative decoding // NOLINT
};
struct common_params_vocoder {
std::string hf_repo = ""; // HF repo // NOLINT
std::string hf_file = ""; // HF file // NOLINT
std::string model = ""; // model path // NOLINT
std::string model_url = ""; // model url to download // NOLINT
};
struct common_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 4096; // context size
@ -193,11 +204,13 @@ struct common_params {
float defrag_thold = 0.1f; // KV cache defragmentation threshold
// offload params
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
@ -211,8 +224,9 @@ struct common_params {
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
struct common_params_sampling sampling;
struct common_params_sampling sampling;
struct common_params_speculative speculative;
struct common_params_vocoder vocoder;
std::string model = ""; // model path // NOLINT
std::string model_alias = ""; // model alias // NOLINT
@ -593,7 +607,8 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si
// Embedding utils
//
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
// TODO: repace embd_norm with an enum
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);

27
common/sampling.cpp
View file

@ -161,32 +161,20 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
params.logit_bias.size(),
params.logit_bias.data()));
llama_sampler_chain_add(result->chain,
llama_sampler_init_penalties(
llama_n_vocab (model),
llama_token_eos(model),
llama_token_nl (model),
params.penalty_last_n,
params.penalty_repeat,
params.penalty_freq,
params.penalty_present,
params.penalize_nl,
params.ignore_eos));
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY:
case COMMON_SAMPLER_TYPE_DRY:
{
std::vector<const char*> c_breakers;
std::vector<const char *> c_breakers;
c_breakers.reserve(params.dry_sequence_breakers.size());
for (const auto& str : params.dry_sequence_breakers) {
for (const auto & str : params.dry_sequence_breakers) {
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
break;
case COMMON_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
break;
@ -208,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
break;
case COMMON_SAMPLER_TYPE_PENALTIES:
llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
@ -415,6 +406,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
case COMMON_SAMPLER_TYPE_XTC: return 'x';
case COMMON_SAMPLER_TYPE_INFILL: return 'i';
case COMMON_SAMPLER_TYPE_PENALTIES: return 'e';
default : return '?';
}
}
@ -429,6 +421,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
case COMMON_SAMPLER_TYPE_XTC: return "xtc";
case COMMON_SAMPLER_TYPE_INFILL: return "infill";
case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties";
default : return "";
}
}
@ -443,6 +436,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
{ "penalties", COMMON_SAMPLER_TYPE_PENALTIES },
};
// since samplers names are written multiple ways
@ -489,6 +483,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES },
};
std::vector<common_sampler_type> samplers;

59
convert_hf_to_gguf.py
View file

@ -221,17 +221,17 @@ class Model:
self.gguf_writer.add_context_length(n_ctx)
logger.info(f"gguf: context length = {n_ctx}")
n_embd = self.find_hparam(["hidden_size", "n_embd"])
self.gguf_writer.add_embedding_length(n_embd)
logger.info(f"gguf: embedding length = {n_embd}")
if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None:
self.gguf_writer.add_embedding_length(n_embd)
logger.info(f"gguf: embedding length = {n_embd}")
if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
logger.info(f"gguf: feed forward length = {n_ff}")
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_head_count(n_head)
logger.info(f"gguf: head count = {n_head}")
if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None:
self.gguf_writer.add_head_count(n_head)
logger.info(f"gguf: head count = {n_head}")
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
@ -296,7 +296,9 @@ class Model:
break
for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
data = data_torch.squeeze().numpy()
# TODO: why do we squeeze here?
# data = data_torch.squeeze().numpy()
data = data_torch.numpy()
# if data ends up empty, it means data_torch was a scalar tensor -> restore
if len(data.shape) == 0:
@ -324,6 +326,8 @@ class Model:
gguf.MODEL_TENSOR.TIME_MIX_W2,
gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
gguf.MODEL_TENSOR.POSNET_NORM1,
gguf.MODEL_TENSOR.POSNET_NORM2,
)
)
or not new_name.endswith(".weight")
@ -689,6 +693,9 @@ class Model:
return res
# Marker: End get_vocab_base_pre
def _set_vocab_none(self) -> None:
self.gguf_writer.add_tokenizer_model("none")
def _set_vocab_gpt2(self) -> None:
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
@ -2027,6 +2034,44 @@ class Qwen2VLModel(Model):
yield name, data
@Model.register("WavTokenizerDec")
class WavTokenizerDecModel(Model):
model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
if \
name.endswith("codebook.cluster_size") or \
name.endswith("codebook.embed_avg") or \
name.endswith("codebook.inited"):
logger.debug(f"Skipping {name!r}")
return []
logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}")
return [(self.map_tensor_name(name), data_torch)]
def set_vocab(self):
self._set_vocab_none()
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_vocab_size (self.hparams["vocab_size"])
self.gguf_writer.add_features_length (self.hparams["n_embd_features"])
self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
self.gguf_writer.add_group_norm_eps (self.hparams["group_norm_epsilon"])
self.gguf_writer.add_group_norm_groups (self.hparams["group_norm_groups"])
self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
self.gguf_writer.add_posnet_block_count (self.hparams["posnet"]["n_layer"])
self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
self.gguf_writer.add_convnext_block_count (self.hparams["convnext"]["n_layer"])
self.gguf_writer.add_causal_attention(False)
@Model.register("Qwen2MoeForCausalLM")
class Qwen2MoeModel(Model):
model_arch = gguf.MODEL_ARCH.QWEN2MOE

1
examples/CMakeLists.txt
View file

@ -51,6 +51,7 @@ else()
add_subdirectory(speculative)
add_subdirectory(speculative-simple)
add_subdirectory(tokenize)
add_subdirectory(tts)
add_subdirectory(gen-docs)
if (NOT GGML_BACKEND_DL)
# these examples use the backends directly and cannot be built with dynamic loading

1
examples/batched/batched.cpp
View file

@ -65,6 +65,7 @@ int main(int argc, char ** argv) {
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
llama_sampler * smpl = llama_sampler_chain_init(sparams);

2
examples/gritlm/gritlm.cpp
View file

@ -75,7 +75,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
}
std::vector<float> emb_norm(emb_unorm.size());
common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2);
result.push_back(emb_norm);
#ifdef GRIT_DEBUG

1
examples/llama.android/llama/build.gradle.kts
View file

@ -19,6 +19,7 @@ android {
externalNativeBuild {
cmake {
arguments += "-DLLAMA_BUILD_COMMON=ON"
arguments += "-DGGML_LLAMAFILE=OFF"
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()

6
examples/llava/clip.cpp
View file

@ -896,7 +896,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
// stride = 1, padding = 1, bias is nullptr
block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
// layer norm
// // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
@ -944,7 +944,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
// block_2
{
// stride = 2
block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
// layer norm
@ -1005,7 +1005,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
// mlp_2 ne [24, 24, 2048, 1]
mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
// weight ne = [3, 3, 2048, 1]
struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));

5
examples/main/README.md
View file

@ -177,16 +177,11 @@ Example usage: `--temp 0`
- `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
- `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
- `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
### DRY Repetition Penalty
DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).

2
examples/retrieval/retrieval.cpp
View file

@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + batch.seq_id[i][0] * n_embd;
common_embd_normalize(embd, out, n_embd);
common_embd_normalize(embd, out, n_embd, 2);
}
}

55
examples/server/README.md
View file

@ -104,7 +104,6 @@ The project is under active development, and we are [looking for feedback and co
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
| `--penalize-nl` | penalize newline tokens (default: false) |
| `--temp N` | temperature (default: 0.8) |
| `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
@ -393,8 +392,6 @@ These words will not be included in the completion, so make sure to add them to
`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
@ -441,19 +438,22 @@ These words will not be included in the completion, so make sure to add them to
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`
`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false`
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.
`timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false`
**Response format**
- Note: In streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
```json
{
"content": "<the token selected by the model>",
"content": "<the token generated by the model>",
"tokens": [ generated token ids if requested ],
"probs": [
{
"prob": float,
@ -471,6 +471,7 @@ These words will not be included in the completion, so make sure to add them to
Notice that each `probs` is an array of length `n_probs`.
- `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request.
- `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
- `model`: The path to the model loaded with `-m`
@ -655,7 +656,6 @@ This endpoint is public (no API key check). By default, it is read-only. To make
"mirostat": 0,
"mirostat_tau": 5.0,
"mirostat_eta": 0.10000000149011612,
"penalize_nl": false,
"stop": [],
"max_tokens": -1,
"n_keep": 0,
@ -763,6 +763,8 @@ curl http://localhost:8080/v1/chat/completions \
### POST `/v1/embeddings`: OpenAI-compatible embeddings API
This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
*Options:*
See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
@ -795,6 +797,46 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r
}'
```
### POST `/embeddings`: non-OpenAI-compatible embeddings API
This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm.
Note that the response format of this endpoint is different from `/v1/embeddings`.
*Options:*
Same as the `/v1/embeddings` endpoint.
*Examples:*
Same as the `/v1/embeddings` endpoint.
**Response format**
```json
[
{
"index": 0,
"embedding": [
[ ... embeddings for token 0 ... ],
[ ... embeddings for token 1 ... ],
[ ... ]
[ ... embeddings for token N-1 ... ],
]
},
...
{
"index": P,
"embedding": [
[ ... embeddings for token 0 ... ],
[ ... embeddings for token 1 ... ],
[ ... ]
[ ... embeddings for token N-1 ... ],
]
}
]
```
### GET `/slots`: Returns the current slots processing state
> [!WARNING]
@ -845,7 +887,6 @@ Example:
"mirostat": 0,
"mirostat_tau": 5.0,
"mirostat_eta": 0.10000000149011612,
"penalize_nl": false,
"stop": [],
"max_tokens": -1,
"n_keep": 0,

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examples/server/public/index.html.gz
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1
examples/server/public_legacy/index-new.html
View file

@ -39,7 +39,6 @@
temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
repeat_last_n: 0, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.0, // 1.0 = disabled
penalize_nl: false, // true only useful for infinite completion
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well

2
examples/server/public_legacy/index.html
View file

@ -303,7 +303,6 @@
temperature: 0.7,
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@ -1006,7 +1005,6 @@
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

164
examples/server/server.cpp
View file

@ -79,8 +79,9 @@ enum error_type {
};
struct slot_params {
bool stream = true;
bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
bool stream = true;
bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
bool return_tokens = false;
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
@ -135,7 +136,6 @@ struct slot_params {
{"mirostat", sampling.mirostat},
{"mirostat_tau", sampling.mirostat_tau},
{"mirostat_eta", sampling.mirostat_eta},
{"penalize_nl", sampling.penalize_nl},
{"stop", antiprompt},
{"max_tokens", n_predict}, // User configured n_predict
{"n_keep", n_keep},
@ -184,6 +184,7 @@ struct server_task {
static slot_params params_from_json_cmpl(
const llama_model * model,
const llama_context * ctx,
const common_params & params_base,
const json & data) {
slot_params params;
@ -199,6 +200,7 @@ struct server_task {
params.stream = json_value(data, "stream", false);
params.cache_prompt = json_value(data, "cache_prompt", true);
params.return_tokens = json_value(data, "return_tokens", false);
params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict));
params.n_indent = json_value(data, "n_indent", defaults.n_indent);
params.n_keep = json_value(data, "n_keep", defaults.n_keep);
@ -226,7 +228,6 @@ struct server_task {
params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat);
params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau);
params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta);
params.sampling.penalize_nl = json_value(data, "penalize_nl", defaults.sampling.penalize_nl);
params.sampling.seed = json_value(data, "seed", defaults.sampling.seed);
params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs);
params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep);
@ -239,8 +240,27 @@ struct server_task {
params.speculative.n_min = std::max(params.speculative.n_min, 2);
params.speculative.n_max = std::max(params.speculative.n_max, 0);
// TODO: add more sanity checks for the input parameters
if (params.sampling.penalty_last_n < -1) {
throw std::runtime_error("Error: repeat_last_n must be >= -1");
}
if (params.sampling.dry_penalty_last_n < -1) {
throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
}
if (params.sampling.penalty_last_n == -1) {
// note: should be the slot's context and not the full context, but it's ok
params.sampling.penalty_last_n = llama_n_ctx(ctx);
}
if (params.sampling.dry_penalty_last_n == -1) {
params.sampling.dry_penalty_last_n = llama_n_ctx(ctx);
}
if (params.sampling.dry_base < 1.0f) {
params.sampling.dry_base = defaults.sampling.dry_base;
params.sampling.dry_base = defaults.sampling.dry_base;
}
// sequence breakers for DRY
@ -450,7 +470,10 @@ struct completion_token_output {
struct server_task_result_cmpl_final : server_task_result {
int index = 0;
std::string content;
std::string content;
llama_tokens tokens;
bool stream;
result_timings timings;
std::string prompt;
@ -492,6 +515,7 @@ struct server_task_result_cmpl_final : server_task_result {
json res = json {
{"index", index},
{"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk
{"tokens", stream ? llama_tokens {} : tokens},
{"id_slot", id_slot},
{"stop", true},
{"model", oaicompat_model},
@ -521,9 +545,9 @@ struct server_task_result_cmpl_final : server_task_result {
json choices = json::array({json{
{"finish_reason", finish_reason},
{"index", 0},
{"message", json{
{"message", json {
{"content", content},
{"role", "assistant"}
{"role", "assistant"}
}
}}});
@ -587,7 +611,9 @@ struct server_task_result_cmpl_final : server_task_result {
struct server_task_result_cmpl_partial : server_task_result {
int index = 0;
std::string content;
std::string content;
llama_tokens tokens;
int32_t n_decoded;
int32_t n_prompt_tokens;
@ -619,6 +645,7 @@ struct server_task_result_cmpl_partial : server_task_result {
json res = json {
{"index", index},
{"content", content},
{"tokens", tokens},
{"stop", false},
{"id_slot", id_slot},
{"tokens_predicted", n_decoded},
@ -660,7 +687,7 @@ struct server_task_result_cmpl_partial : server_task_result {
json second_ret = json{
{"choices", json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"delta", json {
{"content", content}}}
}})},
{"created", t},
@ -675,7 +702,7 @@ struct server_task_result_cmpl_partial : server_task_result {
{"finish_reason", nullptr},
{"index", 0},
{"delta",
json{
json {
{"content", content},
}},
}});
@ -699,32 +726,52 @@ struct server_task_result_cmpl_partial : server_task_result {
struct server_task_result_embd : server_task_result {
int index = 0;
std::vector<float> embedding;
std::vector<std::vector<float>> embedding;
int32_t n_tokens;
// OAI-compat fields
bool oaicompat = false;
virtual int get_index() override {
return index;
}
virtual json to_json() override {
return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat();
}
json to_json_non_oaicompat() {
return json {
{"index", index},
{"embedding", embedding},
};
}
json to_json_oaicompat() {
return json {
{"index", index},
{"embedding", embedding[0]},
{"tokens_evaluated", n_tokens},
};
}
};
struct server_task_result_rerank : server_task_result {
int index = 0;
float score = -1e6;
int32_t n_tokens;
virtual int get_index() override {
return index;
}
virtual json to_json() override {
return json {
{"index", index},
{"score", score},
{"index", index},
{"score", score},
{"tokens_evaluated", n_tokens},
};
}
};
@ -931,8 +978,11 @@ struct server_slot {
size_t last_nl_pos = 0;
std::string generated_text;
std::string generated_text;
llama_tokens generated_tokens;
llama_tokens cache_tokens;
std::vector<completion_token_output> generated_token_probs;
bool has_next_token = true;
@ -976,6 +1026,7 @@ struct server_slot {
n_sent_token_probs = 0;
task_type = SERVER_TASK_TYPE_COMPLETION;
generated_tokens.clear();
generated_token_probs.clear();
}
@ -1469,7 +1520,7 @@ struct server_context {
n_ctx = llama_n_ctx(ctx);
add_bos_token = llama_add_bos_token(model);
has_eos_token = !llama_add_eos_token(model);
has_eos_token = llama_token_eos(model) != LLAMA_TOKEN_NULL;
if (!params_base.speculative.model.empty()) {
SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str());
@ -1716,8 +1767,10 @@ struct server_context {
const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special);
slot.sampled = result.tok;
// search stop word and delete it
slot.generated_text += token_str;
if (slot.params.return_tokens) {
slot.generated_tokens.push_back(result.tok);
}
slot.has_next_token = true;
// check if there is incomplete UTF-8 character at the end
@ -1742,6 +1795,7 @@ struct server_context {
break;
}
// search stop word and delete it
if (!incomplete) {
size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
@ -1894,6 +1948,7 @@ struct server_context {
res->id = slot.id_task;
res->index = slot.index;
res->content = tkn.text_to_send;
res->tokens = { tkn.tok };
res->n_decoded = slot.n_decoded;
res->n_prompt_tokens = slot.n_prompt_tokens;
@ -1934,6 +1989,7 @@ struct server_context {
res->index = slot.index;
res->content = slot.generated_text;
res->tokens = slot.generated_tokens;
res->timings = slot.get_timings();
res->prompt = common_detokenize(ctx, slot.prompt_tokens, true);
@ -1975,8 +2031,10 @@ struct server_context {
void send_embedding(const server_slot & slot, const llama_batch & batch) {
auto res = std::make_unique<server_task_result_embd>();
res->id = slot.id_task;
res->index = slot.index;
res->id = slot.id_task;
res->index = slot.index;
res->n_tokens = slot.n_prompt_tokens;
res->oaicompat = slot.params.oaicompat;
const int n_embd = llama_n_embd(model);
@ -1995,12 +2053,18 @@ struct server_context {
if (embd == NULL) {
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
res->embedding = std::vector<float>(n_embd, 0.0f);
res->embedding.push_back(std::vector<float>(n_embd, 0.0f));
continue;
}
common_embd_normalize(embd, embd_res.data(), n_embd);
res->embedding = embd_res;
// normalize only when there is pooling
// TODO: configurable
if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) {
common_embd_normalize(embd, embd_res.data(), n_embd, 2);
res->embedding.push_back(embd_res);
} else {
res->embedding.push_back({ embd, embd + n_embd });
}
}
SLT_DBG(slot, "%s", "sending embeddings\n");
@ -2012,6 +2076,7 @@ struct server_context {
auto res = std::make_unique<server_task_result_rerank>();
res->id = slot.id_task;
res->index = slot.index;
res->n_tokens = slot.n_prompt_tokens;
for (int i = 0; i < batch.n_tokens; ++i) {
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
@ -2613,7 +2678,10 @@ struct server_context {
// add prompt tokens for processing in the current batch
while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false);
// without pooling, we want to output the embeddings for all the tokens in the batch
const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE;
common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, need_embd);
if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@ -3381,7 +3449,7 @@ int main(int argc, char ** argv) {
task.index = i;
task.prompt_tokens = std::move(tokenized_prompts[i]);
task.params = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.params_base, data);
task.params = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.ctx, ctx_server.params_base, data);
task.id_selected_slot = json_value(data, "id_slot", -1);
// OAI-compat
@ -3621,34 +3689,50 @@ int main(int argc, char ** argv) {
res_ok(res, data);
};
const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, bool oaicompat) {
const json body = json::parse(req.body);
bool oaicompat = false;
// an input prompt can be a string or a list of tokens (integer)
if (oaicompat && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
return;
}
// for the shape of input/content, see tokenize_input_prompts()
json prompt;
if (body.count("input") != 0) {
oaicompat = true;
prompt = body.at("input");
} else if (body.count("content") != 0) {
// with "content", we only support single prompt
prompt = std::vector<std::string>{body.at("content")};
} else if (body.contains("content")) {
oaicompat = false;
prompt = body.at("content");
} else {
res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
return;
}
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
for (const auto & tokens : tokenized_prompts) {
// this check is necessary for models that do not add BOS token to the input
if (tokens.empty()) {
res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST));
return;
}
}
// create and queue the task
json responses = json::array();
bool error = false;
{
std::vector<server_task> tasks;
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, /* add_special */ false, true);
for (size_t i = 0; i < tokenized_prompts.size(); i++) {
server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING);
server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING);
task.id = ctx_server.queue_tasks.get_new_id();
task.index = i;
task.prompt_tokens = std::move(tokenized_prompts[i]);
// OAI-compat
task.params.oaicompat = oaicompat;
tasks.push_back(task);
}
@ -3676,12 +3760,18 @@ int main(int argc, char ** argv) {
}
// write JSON response
json root = oaicompat
? format_embeddings_response_oaicompat(body, responses)
: responses.size() == 1 ? responses[0] : json(responses);
json root = oaicompat ? format_embeddings_response_oaicompat(body, responses) : json(responses);
res_ok(res, root);
};
const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
handle_embeddings_impl(req, res, false);
};
const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
handle_embeddings_impl(req, res, true);
};
const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) {
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED));
@ -3855,7 +3945,7 @@ int main(int argc, char ** argv) {
svr->Post("/infill", handle_infill);
svr->Post("/embedding", handle_embeddings); // legacy
svr->Post("/embeddings", handle_embeddings);
svr->Post("/v1/embeddings", handle_embeddings);
svr->Post("/v1/embeddings", handle_embeddings_oai);
svr->Post("/rerank", handle_rerank);
svr->Post("/reranking", handle_rerank);
svr->Post("/v1/rerank", handle_rerank);

16
examples/server/tests/unit/test_completion.py
View file

@ -10,16 +10,17 @@ def create_server():
global server
server = ServerPreset.tinyllama2()
@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False),
("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False),
@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [
("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False),
("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True),
])
def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool):
def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool):
global server
server.start()
res = server.make_request("POST", "/completion", data={
"n_predict": n_predict,
"prompt": prompt,
"return_tokens": return_tokens,
})
assert res.status_code == 200
assert res.body["timings"]["prompt_n"] == n_prompt
@ -27,6 +28,11 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int,
assert res.body["truncated"] == truncated
assert type(res.body["has_new_line"]) == bool
assert match_regex(re_content, res.body["content"])
if return_tokens:
assert len(res.body["tokens"]) > 0
assert all(type(tok) == int for tok in res.body["tokens"])
else:
assert res.body["tokens"] == []
@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
@ -56,6 +62,8 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp
assert data["generation_settings"]["seed"] == server.seed
assert match_regex(re_content, content)
else:
assert len(data["tokens"]) > 0
assert all(type(tok) == int for tok in data["tokens"])
content += data["content"]

108
examples/server/tests/unit/test_embedding.py
View file

@ -14,8 +14,9 @@ def create_server():
def test_embedding_single():
global server
server.pooling = 'last'
server.start()
res = server.make_request("POST", "/embeddings", data={
res = server.make_request("POST", "/v1/embeddings", data={
"input": "I believe the meaning of life is",
})
assert res.status_code == 200
@ -29,8 +30,9 @@ def test_embedding_single():
def test_embedding_multiple():
global server
server.pooling = 'last'
server.start()
res = server.make_request("POST", "/embeddings", data={
res = server.make_request("POST", "/v1/embeddings", data={
"input": [
"I believe the meaning of life is",
"Write a joke about AI from a very long prompt which will not be truncated",
@ -45,10 +47,69 @@ def test_embedding_multiple():
assert len(d['embedding']) > 1
def test_embedding_openai_library_single():
@pytest.mark.parametrize(
"input,is_multi_prompt",
[
# single prompt
("string", False),
([12, 34, 56], False),
([12, 34, "string", 56, 78], False),
# multiple prompts
(["string1", "string2"], True),
(["string1", [12, 34, 56]], True),
([[12, 34, 56], [12, 34, 56]], True),
([[12, 34, 56], [12, "string", 34, 56]], True),
]
)
def test_embedding_mixed_input(input, is_multi_prompt: bool):
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
res = server.make_request("POST", "/v1/embeddings", data={"input": input})
assert res.status_code == 200
data = res.body['data']
if is_multi_prompt:
assert len(data) == len(input)
for d in data:
assert 'embedding' in d
assert len(d['embedding']) > 1
else:
assert 'embedding' in data[0]
assert len(data[0]['embedding']) > 1
def test_embedding_pooling_none():
global server
server.pooling = 'none'
server.start()
res = server.make_request("POST", "/embeddings", data={
"input": "hello hello hello",
})
assert res.status_code == 200
assert 'embedding' in res.body[0]
assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special
# make sure embedding vector is not normalized
for x in res.body[0]['embedding']:
assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON
def test_embedding_pooling_none_oai():
global server
server.pooling = 'none'
server.start()
res = server.make_request("POST", "/v1/embeddings", data={
"input": "hello hello hello",
})
# /v1/embeddings does not support pooling type 'none'
assert res.status_code == 400
def test_embedding_openai_library_single():
global server
server.pooling = 'last'
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is")
assert len(res.data) == 1
assert len(res.data[0].embedding) > 1
@ -56,8 +117,9 @@ def test_embedding_openai_library_single():
def test_embedding_openai_library_multiple():
global server
server.pooling = 'last'
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
res = client.embeddings.create(model="text-embedding-3-small", input=[
"I believe the meaning of life is",
"Write a joke about AI from a very long prompt which will not be truncated",
@ -71,8 +133,9 @@ def test_embedding_openai_library_multiple():
def test_embedding_error_prompt_too_long():
global server
server.pooling = 'last'
server.start()
res = server.make_request("POST", "/embeddings", data={
res = server.make_request("POST", "/v1/embeddings", data={
"input": "This is a test " * 512,
})
assert res.status_code != 200
@ -80,8 +143,9 @@ def test_embedding_error_prompt_too_long():
def test_same_prompt_give_same_result():
server.pooling = 'last'
server.start()
res = server.make_request("POST", "/embeddings", data={
res = server.make_request("POST", "/v1/embeddings", data={
"input": [
"I believe the meaning of life is",
"I believe the meaning of life is",
@ -97,3 +161,33 @@ def test_same_prompt_give_same_result():
vi = res.body['data'][i]['embedding']
for x, y in zip(v0, vi):
assert abs(x - y) < EPSILON
@pytest.mark.parametrize(
"content,n_tokens",
[
("I believe the meaning of life is", 9),
("This is a test", 6),
]
)
def test_embedding_usage_single(content, n_tokens):
global server
server.start()
res = server.make_request("POST", "/v1/embeddings", data={"input": content})
assert res.status_code == 200
assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
assert res.body['usage']['prompt_tokens'] == n_tokens
def test_embedding_usage_multiple():
global server
server.start()
res = server.make_request("POST", "/v1/embeddings", data={
"input": [
"I believe the meaning of life is",
"I believe the meaning of life is",
],
})
assert res.status_code == 200
assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
assert res.body['usage']['prompt_tokens'] == 2 * 9

23
examples/server/tests/unit/test_rerank.py
View file

@ -53,3 +53,26 @@ def test_invalid_rerank_req(documents):
})
assert res.status_code == 400
assert "error" in res.body
@pytest.mark.parametrize(
"query,doc1,doc2,n_tokens",
[
("Machine learning is", "A machine", "Learning is", 19),
("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
]
)
def test_rerank_usage(query, doc1, doc2, n_tokens):
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": query,
"documents": [
doc1,
doc2,
]
})
assert res.status_code == 200
assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
assert res.body['usage']['prompt_tokens'] == n_tokens

3
examples/server/tests/utils.py
View file

@ -65,6 +65,7 @@ class ServerProcess:
server_reranking: bool | None = False
server_metrics: bool | None = False
server_slots: bool | None = False
pooling: str | None = None
draft: int | None = None
api_key: str | None = None
response_format: str | None = None
@ -132,6 +133,8 @@ class ServerProcess:
server_args.append("--metrics")
if self.server_slots:
server_args.append("--slots")
if self.pooling:
server_args.extend(["--pooling", self.pooling])
if self.model_alias:
server_args.extend(["--alias", self.model_alias])
if self.n_ctx:

2
examples/server/themes/buttons-top/index.html
View file

@ -222,7 +222,6 @@
temperature: 0.7,
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
@ -779,7 +778,6 @@
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

2
examples/server/themes/wild/index.html
View file

@ -225,7 +225,6 @@
temperature: 0.7,
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
@ -782,7 +781,6 @@
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

19
examples/server/utils.hpp
View file

@ -138,6 +138,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_
* and multiple prompts (multi-tasks):
* - "prompt": ["string1", "string2"]
* - "prompt": ["string1", [12, 34, 56]]
* - "prompt": [[12, 34, 56], [78, 90, 12]]
* - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
*/
static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
@ -560,6 +561,7 @@ static json oaicompat_completion_params_parse(
static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
json data = json::array();
int32_t n_tokens = 0;
int i = 0;
for (const auto & elem : embeddings) {
data.push_back(json{
@ -567,14 +569,16 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
{"index", i++},
{"object", "embedding"}
});
n_tokens += json_value(elem, "tokens_evaluated", 0);
}
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json { // TODO: fill
{"prompt_tokens", 0},
{"total_tokens", 0}
{"usage", json {
{"prompt_tokens", n_tokens},
{"total_tokens", n_tokens}
}},
{"data", data}
};
@ -584,20 +588,23 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
static json format_response_rerank(const json & request, const json & ranks) {
json data = json::array();
int32_t n_tokens = 0;
int i = 0;
for (const auto & rank : ranks) {
data.push_back(json{
{"index", i++},
{"relevance_score", json_value(rank, "score", 0.0)},
});
n_tokens += json_value(rank, "tokens_evaluated", 0);
}
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json { // TODO: fill
{"prompt_tokens", 0},
{"total_tokens", 0}
{"usage", json {
{"prompt_tokens", n_tokens},
{"total_tokens", n_tokens}
}},
{"results", data}
};

7
examples/server/webui/package-lock.json generated
View file

@ -8,6 +8,7 @@
"name": "webui",
"version": "0.0.0",
"dependencies": {
"@sec-ant/readable-stream": "^0.6.0",
"@vscode/markdown-it-katex": "^1.1.1",
"autoprefixer": "^10.4.20",
"daisyui": "^4.12.14",
@ -617,6 +618,12 @@
"win32"
]
},
"node_modules/@sec-ant/readable-stream": {
"version": "0.6.0",
"resolved": "https://registry.npmjs.org/@sec-ant/readable-stream/-/readable-stream-0.6.0.tgz",
"integrity": "sha512-uiBh8DrB5FN35gP6/o8JEhEQ7/ci1jUsOZO/VMUjyvTpjtV54VstOXVj1TvTj/wsT23pfX6butxxh3qufsW3+g==",
"license": "MIT"
},
"node_modules/@vscode/markdown-it-katex": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/@vscode/markdown-it-katex/-/markdown-it-katex-1.1.1.tgz",

1
examples/server/webui/package.json
View file

@ -14,6 +14,7 @@
"vite": "^5.4.10"
},
"dependencies": {
"@sec-ant/readable-stream": "^0.6.0",
"@vscode/markdown-it-katex": "^1.1.1",
"autoprefixer": "^10.4.20",
"daisyui": "^4.12.14",

11
examples/server/webui/src/main.js
View file

@ -5,13 +5,16 @@ import TextLineStream from 'textlinestream';
// math formula rendering
import 'katex/dist/katex.min.css';
import markdownItKatexGpt, { renderLatexHTML } from './katex-gpt';
import markdownItKatexGpt from './katex-gpt';
import markdownItKatexNormal from '@vscode/markdown-it-katex';
// code highlighting
import hljs from './highlight-config';
import daisyuiThemes from 'daisyui/src/theming/themes';
// ponyfill for missing ReadableStream asyncIterator on Safari
import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
const isDev = import.meta.env.MODE === 'development';
// utility functions
@ -33,7 +36,7 @@ const CONFIG_DEFAULT = {
systemMessage: 'You are a helpful assistant.',
showTokensPerSecond: false,
// make sure these default values are in sync with `common.h`
samplers: 'dkypmxt',
samplers: 'edkypmxt',
temperature: 0.8,
dynatemp_range: 0.0,
dynatemp_exponent: 1.0,
@ -283,7 +286,7 @@ async function* sendSSEPostRequest(url, fetchOptions) {
const lines = res.body
.pipeThrough(new TextDecoderStream())
.pipeThrough(new TextLineStream());
for await (const line of lines) {
for await (const line of asyncIterator(lines)) {
if (isDev) console.log({line});
if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
const data = JSON.parse(line.slice(5));
@ -442,7 +445,7 @@ const mainApp = createApp({
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': this.config.apiKey ? `Bearer ${this.config.apiKey}` : undefined,
...(this.config.apiKey ? {'Authorization': `Bearer ${this.config.apiKey}`} : {})
},
body: JSON.stringify(params),
signal: abortController.signal,

5
examples/tts/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET llama-tts)
add_executable(${TARGET} tts.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

180
examples/tts/convert_pt_to_hf.py Normal file
View file

@ -0,0 +1,180 @@
# convert the https://huggingface.co/novateur/WavTokenizer-large-speech-75token to HF format
# the goal is to be able to reuse the convert_hf_to_gguf.py after that to create a GGUF file with the WavTokenizer decoder
#
# TODO: this script is LLM-generated and probably very inefficient and should be rewritten
import torch
import json
import os
import sys
import re
from safetensors.torch import save_file
# default
model_path = './model.pt';
# read from CLI
if len(sys.argv) > 1:
model_path = sys.argv[1]
# get the directory of the input model
path_dst = os.path.dirname(model_path)
print(f"Loading model from {model_path}")
model = torch.load(model_path, map_location='cpu')
#print(model)
# print all keys
for key in model.keys():
print(key)
if key == 'hyper_parameters':
#print(model[key])
# dump as json pretty
print(json.dumps(model[key], indent=4))
#if key != 'state_dict' and key != 'optimizer_states':
# print(model[key])
# Check if the loaded model is a state_dict or a model instance
if isinstance(model, torch.nn.Module):
state_dict = model.state_dict()
else:
state_dict = model
# Print the structure of the state_dict to understand its format
print("State dictionary keys:")
for key in state_dict.keys():
print(key)
# Ensure the state_dict is flat and contains only torch.Tensor objects
def flatten_state_dict(state_dict, parent_key='', sep='.'):
items = []
items_new = []
for k, v in state_dict.items():
new_key = f"{parent_key}{sep}{k}" if parent_key else k
if isinstance(v, torch.Tensor):
items.append((new_key, v))
elif isinstance(v, dict):
items.extend(flatten_state_dict(v, new_key, sep=sep).items())
return dict(items)
size_total_mb = 0
for key, value in list(items):
# keep only what we need for inference
if not key.startswith('state_dict.feature_extractor.encodec.quantizer.') and \
not key.startswith('state_dict.backbone.') and \
not key.startswith('state_dict.head.out'):
print('Skipping key: ', key)
continue
new_key = key
new_key = new_key.replace('state_dict.', '')
new_key = new_key.replace('pos_net', 'posnet')
# check if matches "backbone.posnet.%d.bias" or "backbone.posnet.%d.weight"
if new_key.startswith("backbone.posnet."):
match = re.match(r"backbone\.posnet\.(\d+)\.(bias|weight)", new_key)
if match:
new_key = f"backbone.posnet.{match.group(1)}.norm.{match.group(2)}"
# "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" -> "backbone.embedding.weight"
if new_key == "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed":
new_key = "backbone.embedding.weight"
# these are the only rows used
# ref: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/wav_tokenizer/audio_codec.py#L100
if new_key.endswith("norm.scale.weight"):
new_key = new_key.replace("norm.scale.weight", "norm.weight")
value = value[0]
if new_key.endswith("norm.shift.weight"):
new_key = new_key.replace("norm.shift.weight", "norm.bias")
value = value[0]
if new_key.endswith("gamma"):
new_key = new_key.replace("gamma", "gamma.weight")
# convert from 1D [768] to 2D [768, 1] so that ggml_add can broadcast the bias
if (new_key.endswith("norm.weight") or new_key.endswith("norm1.weight") or new_key.endswith("norm2.weight") or new_key.endswith(".bias")) and (new_key.startswith("backbone.posnet") or new_key.startswith("backbone.embed.bias")):
value = value.unsqueeze(1)
if new_key.endswith("dwconv.bias"):
value = value.unsqueeze(1)
size_mb = value.element_size() * value.nelement() / (1024 * 1024)
print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}")
size_total_mb += size_mb
#print(key, '->', new_key, ': ', value)
#print(key, '->', new_key)
items_new.append((new_key, value))
print(f"Total size: {size_total_mb:8.2f} MB")
return dict(items_new)
flattened_state_dict = flatten_state_dict(state_dict)
# Convert the model to the safetensors format
output_path = path_dst + '/model.safetensors'
save_file(flattened_state_dict, output_path)
print(f"Model has been successfully converted and saved to {output_path}")
# Calculate the total size of the .safetensors file
total_size = os.path.getsize(output_path)
# Create the weight map
weight_map = {
"model.safetensors": ["*"] # Assuming all weights are in one file
}
# Create metadata for the index.json file
metadata = {
"total_size": total_size,
"weight_map": weight_map
}
# Save the metadata to index.json
index_path = path_dst + '/index.json'
with open(index_path, 'w') as f:
json.dump(metadata, f, indent=4)
print(f"Metadata has been saved to {index_path}")
config = {
"architectures": [
"WavTokenizerDec"
],
"hidden_size": 1282,
"n_embd_features": 512,
"n_ff": 2304,
"vocab_size": 4096,
"n_head": 1,
"layer_norm_epsilon": 1e-6,
"group_norm_epsilon": 1e-6,
"group_norm_groups": 32,
"max_position_embeddings": 8192, # ?
"n_layer": 12,
"posnet": {
"n_embd": 768,
"n_layer": 6
},
"convnext": {
"n_embd": 768,
"n_layer": 12
},
}
with open(path_dst + '/config.json', 'w') as f:
json.dump(config, f, indent=4)
print(f"Config has been saved to {path_dst + 'config.json'}")

175
examples/tts/tts-outetts.py Normal file
View file

@ -0,0 +1,175 @@
import sys
#import json
#import struct
import requests
import re
def process_text(text: str):
text = re.sub(r'\d+(\.\d+)?', lambda x: x.group(), text.lower()) # TODO this needs to be fixed
text = re.sub(r'[-_/,\.\\]', ' ', text)
text = re.sub(r'[^a-z\s]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text.split()
# usage:
# python tts-outetts.py http://server-llm:port http://server-dec:port "text"
if len(sys.argv) <= 3:
print("usage: python tts-outetts.py http://server-llm:port http://server-dec:port \"text\"")
exit(1)
host_llm = sys.argv[1]
host_dec = sys.argv[2]
text = sys.argv[3]
prefix = """<|im_start|>
<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>"""
words = process_text(text)
words = "<|text_sep|>".join([i.strip() for i in words])
words += "<|text_end|>\n"
# voice data
# TODO: load from json
#suffix = """<|audio_start|>
#the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
#overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
#package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
#from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
#just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
#two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
#people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
#is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
#pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
#remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
#sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
#i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
#have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
#some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
#critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
#about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
#some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
#of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
#the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
#gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
#aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
#but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
#its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
#still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
#really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
#enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
#and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
#it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
#looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
#lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>"""
# TODO: tokenization is slow for some reason - here is pre-tokenized input
suffix = [ 151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585, 152460, 153375, 151670, 198, 74455,
155808, 151669, 151799, 151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470, 151970, 153413,
152419, 153334, 153289, 153374, 153199, 152040, 153260, 152721, 152680, 153297, 152419, 153248, 152400,
152691, 153368, 153437, 151670, 198, 1722, 155828, 151669, 152607, 152256, 152991, 152299, 152688, 153163,
153016, 152789, 153198, 152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207, 152461, 153321,
153309, 151750, 152137, 153340, 152573, 152267, 153347, 151789, 152681, 153339, 151992, 152512, 151751,
152179, 153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904, 152311, 151670, 198, 1499, 155791,
151669, 152276, 152454, 153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226, 153043, 152325,
153267, 152622, 151670, 198, 4250, 155797, 151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213, 152112, 153204, 151722, 152542, 151670, 198,
19789, 155796, 151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002, 152191, 151734, 152312, 152810,
152237, 153224, 153169, 153224, 152244, 153387, 153404, 151670, 198, 16069, 155811, 151669, 152265, 151946,
151808, 152412, 152363, 152305, 153156, 152733, 152810, 153157, 152016, 152100, 152069, 153234, 152317,
152589, 152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504, 153376, 152272, 152433, 152325,
151941, 151670, 198, 285, 155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381, 152474, 152680,
152157, 153255, 152324, 151682, 151670, 198, 32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488, 153070, 151883, 152890, 152489, 153144,
153375, 152358, 151685, 152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669, 151902, 152720,
153377, 152027, 152378, 152821, 153207, 153459, 153028, 153068, 152507, 153255, 152158, 152921, 151958,
152609, 152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470, 152606, 152162, 152186, 153071,
152244, 153118, 153375, 153018, 152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736, 153380,
153502, 152702, 152115, 153181, 152735, 153277, 153457, 152393, 153112, 152595, 151670, 198, 19098, 155808,
151669, 152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239, 153163, 152922, 153402, 152034,
152591, 153438, 152215, 151673, 152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482, 152718,
152862, 153347, 151670, 198, 72, 155780, 151669, 151795, 152111, 152746, 152377, 153471, 152309, 151670, 198,
19016, 155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701, 152939, 152536, 152091, 151815, 152733,
151672, 151670, 198, 14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042, 153504, 152589, 153333,
151839, 151941, 153038, 153180, 151670, 198, 36996, 8303, 155832, 151669, 152231, 152256, 152835, 152801,
152985, 153400, 152393, 152818, 152765, 152249, 152600, 151699, 152302, 152752, 153018, 153009, 151992,
153054, 152847, 153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458, 152048, 152757, 152428,
153195, 151906, 153006, 153178, 153250, 152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
152228, 152733, 151670, 198, 9096, 155801, 151669, 151698, 153321, 152217, 153039, 152935, 153400, 152122,
152531, 153106, 152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851, 152901, 152885, 152594,
153446, 153080, 151670, 198, 14689, 155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191, 151673,
151690, 151698, 152714, 152846, 152981, 153171, 153384, 153364, 153188, 153246, 151670, 198, 1055, 155779,
151669, 151869, 152388, 152711, 153334, 151736, 151670, 198, 1782, 155780, 151669, 153483, 153240, 152241,
152558, 152697, 153046, 151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605, 153034, 153434,
153372, 153347, 151887, 152453, 152758, 152133, 152510, 152694, 152431, 152321, 153088, 152676, 152223,
152581, 152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032, 152903, 152859, 152989, 151748,
152669, 152661, 152650, 152409, 151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469, 152988,
152894, 151819, 152391, 153019, 152058, 153062, 153230, 151826, 152112, 152306, 152264, 152769, 153390,
152384, 152435, 152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540, 151919, 151893, 152558,
152817, 152946, 152956, 152129, 152715, 153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
151670, 198, 8088, 155792, 151669, 152452, 153497, 153353, 152679, 152533, 152382, 152374, 152611, 153341,
153163, 152285, 153411, 152495, 153141, 152320, 151670, 198, 1199, 155781, 151669, 151764, 152360, 153295,
152634, 153342, 152199, 152271, 151670, 198, 43366, 155799, 151669, 152308, 151682, 152889, 152016, 152385,
152629, 152495, 151826, 153321, 152958, 152180, 151886, 153432, 152922, 152128, 153024, 153040, 152593,
152287, 151677, 151670, 198, 53660, 155808, 151669, 151727, 152092, 152680, 153331, 151699, 152316, 152938,
152289, 152433, 153384, 151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691, 152489, 151941,
152049, 152034, 153053, 152179, 153160, 151676, 153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234, 153135, 152291, 153235, 152143, 152583,
152402, 153483, 152678, 152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825, 152548, 153442,
152109, 152659, 153325, 152781, 152570, 152957, 151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174, 151792, 153409, 153327, 152990, 151670, 198,
275, 155781, 151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974, 151670, 198, 94273, 155799,
151669, 152953, 152938, 153427, 152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331, 152257,
152987, 152777, 153448, 152408, 151696, 152408, 152326, 152699, 151670, 198, 385, 16239, 155828, 151669,
152306, 152268, 153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110, 152918, 152923, 152467,
152331, 153053, 153330, 151889, 153444, 152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499, 152109, 152255, 151739, 152267, 152759,
153318, 153165, 153349, 151670, ]
response = requests.post(
host_llm + "/completion",
json={
"prompt": [prefix + words, *suffix],
"n_predict": 1024,
"cache_prompt": True,
"return_tokens": True,
"samplers": ["top_k"],
"top_k": 16,
"seed": 1003,
}
)
response_json = response.json()
#print(json.dumps(response_json, indent=4))
#print(json.dumps(response_json["prompt"], indent=4).replace("\\n", "\n"))
#print(json.dumps(response_json["timings"], indent=4))
#print(json.dumps(response_json["tokens"], indent=4))
codes = response_json["tokens"]
codes = [t - 151672 for t in codes if t >= 151672 and t <= 155772]
response = requests.post(
host_dec + "/embeddings",
json={
"input": [*codes],
}
)
response_json = response.json()
#print(json.dumps(response_json, indent=4))
# spectrogram
embd = response_json[0]["embedding"]
n_codes = len(embd)
n_embd = len(embd[0])
print('spectrogram generated: n_codes: %d, n_embd: %d' % (n_codes, n_embd))
# post-process the spectrogram to convert to audio
# TODO: see the tts.cpp:embd_to_audio() and implement it in Python
print('converting to audio ...')
print('TODO: see the tts.cpp:embd_to_audio() and implement it in Python')

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#include "arg.h"
#include "common.h"
#include "sampling.h"
#include "log.h"
#include "llama.h"
#define _USE_MATH_DEFINES // For M_PI on MSVC
#include <algorithm>
#include <cmath>
#include <cstdio>
#include <fstream>
#include <map>
#include <regex>
#include <string>
#include <thread>
#include <vector>
//
// Terminal utils
//
#define SQR(X) ((X) * (X))
#define UNCUBE(x) x < 48 ? 0 : x < 115 ? 1 : (x - 35) / 40
/**
* Quantizes 24-bit RGB to xterm256 code range [16,256).
*/
static int rgb2xterm256(int r, int g, int b) {
unsigned char cube[] = {0, 0137, 0207, 0257, 0327, 0377};
int av, ir, ig, ib, il, qr, qg, qb, ql;
av = r * .299 + g * .587 + b * .114 + .5;
ql = (il = av > 238 ? 23 : (av - 3) / 10) * 10 + 8;
qr = cube[(ir = UNCUBE(r))];
qg = cube[(ig = UNCUBE(g))];
qb = cube[(ib = UNCUBE(b))];
if (SQR(qr - r) + SQR(qg - g) + SQR(qb - b) <=
SQR(ql - r) + SQR(ql - g) + SQR(ql - b))
return ir * 36 + ig * 6 + ib + 020;
return il + 0350;
}
static std::string set_xterm256_foreground(int r, int g, int b) {
int x = rgb2xterm256(r, g, b);
std::ostringstream oss;
oss << "\033[38;5;" << x << "m";
return oss.str();
}
const std::vector<std::string> k_colors = {
set_xterm256_foreground(220, 5, 12),
set_xterm256_foreground(232, 96, 28),
set_xterm256_foreground(241, 147, 45),
set_xterm256_foreground(246, 193, 65),
set_xterm256_foreground(247, 240, 86),
set_xterm256_foreground(144, 201, 135),
set_xterm256_foreground( 78, 178, 101),
};
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s -m model.gguf -p \"Hello!\"\n", argv[0]);
LOG("\n");
}
struct wav_header {
char riff[4] = {'R', 'I', 'F', 'F'};
uint32_t chunk_size;
char wave[4] = {'W', 'A', 'V', 'E'};
char fmt[4] = {'f', 'm', 't', ' '};
uint32_t fmt_chunk_size = 16;
uint16_t audio_format = 1; // PCM
uint16_t num_channels = 1; // Mono
uint32_t sample_rate;
uint32_t byte_rate;
uint16_t block_align;
uint16_t bits_per_sample = 16;
char data[4] = {'d', 'a', 't', 'a'};
uint32_t data_size;
};
static void save_wav16(const std::string & fname, const std::vector<float> & data, int sample_rate) {
std::ofstream file(fname, std::ios::binary);
if (!file) {
LOG_ERR("%s: Failed to open file '%s' for writing", __func__, fname.c_str());
return;
}
wav_header header;
header.sample_rate = sample_rate;
header.byte_rate = header.sample_rate * header.num_channels * (header.bits_per_sample / 8);
header.block_align = header.num_channels * (header.bits_per_sample / 8);
header.data_size = data.size() * (header.bits_per_sample / 8);
header.chunk_size = 36 + header.data_size;
file.write(reinterpret_cast<const char*>(&header), sizeof(header));
for (const auto & sample : data) {
int16_t pcm_sample = static_cast<int16_t>(std::clamp(sample * 32767.0, -32768.0, 32767.0));
file.write(reinterpret_cast<const char*>(&pcm_sample), sizeof(pcm_sample));
}
file.close();
}
static void fill_hann_window(int length, bool periodic, float * output) {
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
}
}
// very poor-man fft
static void twiddle(float * real, float * imag, int k, int N) {
float angle = 2 * M_PI * k / N;
*real = cos(angle);
*imag = sin(angle);
}
static void irfft(int n, const float * inp_cplx, float * out_real) {
int N = n / 2 + 1;
std::vector<float> real_input(N);
std::vector<float> imag_input(N);
for (int i = 0; i < N; ++i) {
real_input[i] = inp_cplx[2 * i];
imag_input[i] = inp_cplx[2 * i + 1];
}
std::vector<float> real_output(n);
std::vector<float> imag_output(n);
for (int k = 0; k < n; ++k) {
real_output[k] = 0.0f;
imag_output[k] = 0.0f;
for (int m = 0; m < N; ++m) {
float twiddle_real;
float twiddle_imag;
twiddle(&twiddle_real, &twiddle_imag, k * m, n);
real_output[k] += real_input[m] * twiddle_real - imag_input[m] * twiddle_imag;
imag_output[k] += real_input[m] * twiddle_imag + imag_input[m] * twiddle_real;
}
}
for (int i = 0; i < n; ++i) {
out_real[i] = real_output[i] / N;
}
}
//
// y = torch.nn.functional.fold(
// data, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length),
// )[:, 0, 0, pad:-pad]
//
// data.shape = torch.Size([1, 1280, 261])
// output_size = 84480
// win_length = 1280
// hop_length = 320
// pad = 480
//
static void fold(const std::vector<float> & data, int64_t n_out, int64_t n_win, int64_t n_hop, int64_t n_pad, std::vector<float> & output) {
int64_t output_height = n_out;
int64_t kernel_w = n_win;
int64_t stride_w = n_hop;
int64_t width = n_out;
output.resize(width, 0.0f);
int64_t col_idx = 0;
for (int64_t w_col = 0; w_col < width; ++w_col) {
int64_t start = w_col * stride_w - n_pad;
int64_t end = start + kernel_w;
for (int64_t w_im = start; w_im < end; ++w_im) {
if (w_im >= 0 && w_im < output_height && col_idx < (int64_t) data.size()) {
output[w_im] += data[col_idx];
}
col_idx++;
}
}
output.resize(n_out - 2 * n_pad);
}
// TODO: not optimized at all
static std::vector<float> embd_to_audio(
const float * embd,
const int n_codes,
const int n_embd,
const int n_thread) {
const int n_fft = 1280;
const int n_hop = 320;
const int n_win = 1280;
const int n_pad = (n_win - n_hop)/2;
const int n_out = (n_codes - 1)*n_hop + n_win;
std::vector<float> hann(n_fft);
fill_hann_window(hann.size(), true, hann.data());
int n_spec = n_embd*n_codes;
std::vector<float> E (n_spec);
std::vector<float> S (n_spec);
std::vector<float> ST(n_spec);
for (int l = 0; l < n_codes; ++l) {
for (int k = 0; k < n_embd; ++k) {
E[k*n_codes + l] = embd[l*n_embd + k];
}
}
for (int k = 0; k < n_embd/2; ++k) {
for (int l = 0; l < n_codes; ++l) {
float mag = E[(k )*n_codes + l];
float phi = E[(k + n_embd/2)*n_codes + l];
mag = exp(mag);
if (mag > 1e2) {
mag = 1e2;
}
S[2*(k*n_codes + l) + 0] = mag*cosf(phi);
S[2*(k*n_codes + l) + 1] = mag*sinf(phi);
}
}
for (int l = 0; l < n_codes; ++l) {
for (int k = 0; k < n_embd/2; ++k) {
ST[l*n_embd + 2*k + 0] = S[2*(k*n_codes + l) + 0];
ST[l*n_embd + 2*k + 1] = S[2*(k*n_codes + l) + 1];
}
}
std::vector<float> res (n_codes*n_fft);
std::vector<float> hann2(n_codes*n_fft);
std::vector<std::thread> workers(n_thread);
for (int i = 0; i < n_thread; ++i) {
workers[i] = std::thread([&, i]() {
for (int l = i; l < n_codes; l += n_thread) {
irfft(n_fft, ST.data() + l*n_embd, res.data() + l*n_fft);
for (int j = 0; j < n_fft; ++j) {
res [l*n_fft + j] *= hann[j];
hann2[l*n_fft + j] = hann[j] * hann[j];
}
}
});
}
for (int i = 0; i < n_thread; ++i) {
workers[i].join();
}
std::vector<float> audio;
std::vector<float> env;
fold(res, n_out, n_win, n_hop, n_pad, audio);
fold(hann2, n_out, n_win, n_hop, n_pad, env); // TODO: can be done once
for (size_t i = 0; i < audio.size(); ++i) {
audio[i] /= env[i];
}
return audio;
}
static const std::map<int, std::string> ones = {
{0, "zero"}, {1, "one"}, {2, "two"}, {3, "three"}, {4, "four"},
{5, "five"}, {6, "six"}, {7, "seven"}, {8, "eight"}, {9, "nine"},
{10, "ten"}, {11, "eleven"}, {12, "twelve"}, {13, "thirteen"}, {14, "fourteen"},
{15, "fifteen"}, {16, "sixteen"}, {17, "seventeen"}, {18, "eighteen"}, {19, "nineteen"}
};
static const std::map<int, std::string> tens = {
{2, "twenty"}, {3, "thirty"}, {4, "forty"}, {5, "fifty"},
{6, "sixty"}, {7, "seventy"}, {8, "eighty"}, {9, "ninety"}
};
// Convert a number less than 1000 to words
static std::string convert_less_than_thousand(int num) {
std::string result;
if (num >= 100) {
result += ones.at(num / 100) + " hundred ";
num %= 100;
}
if (num >= 20) {
result += tens.at(num / 10);
if (num % 10 > 0) {
result += "-" + ones.at(num % 10);
}
} else if (num > 0) {
result += ones.at(num);
}
return result;
}
static std::string number_to_words(const std::string & number_str) {
try {
size_t decimal_pos = number_str.find('.');
std::string integer_part = number_str.substr(0, decimal_pos);
int int_number = std::stoi(integer_part);
std::string result;
if (int_number == 0) {
result = "zero";
} else {
if (int_number >= 1000000000) {
int billions = int_number / 1000000000;
result += convert_less_than_thousand(billions) + " billion ";
int_number %= 1000000000;
}
if (int_number >= 1000000) {
int millions = int_number / 1000000;
result += convert_less_than_thousand(millions) + " million ";
int_number %= 1000000;
}
if (int_number >= 1000) {
int thousands = int_number / 1000;
result += convert_less_than_thousand(thousands) + " thousand ";
int_number %= 1000;
}
if (int_number > 0) {
result += convert_less_than_thousand(int_number);
}
}
// Handle decimal part
if (decimal_pos != std::string::npos) {
result += " point";
std::string decimal_part = number_str.substr(decimal_pos + 1);
for (char digit : decimal_part) {
result += " " + ones.at(digit - '0');
}
}
return result;
} catch (const std::exception& e) {
// Skip if fails
return " ";
}
}
static std::string replace_numbers_with_words(const std::string & input_text) {
std::regex number_pattern(R"(\d+(\.\d+)?)");
std::string result;
auto it = std::sregex_iterator(input_text.begin(), input_text.end(), number_pattern);
auto end = std::sregex_iterator();
size_t last_pos = 0;
for (std::sregex_iterator i = it; i != end; ++i) {
const std::smatch& match = *i;
result.append(input_text, last_pos, match.position() - last_pos);
result.append(number_to_words(match.str()));
last_pos = match.position() + match.length();
}
result.append(input_text, last_pos);
return result;
}
// Based on: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/version/v1/prompt_processor.py#L39
static std::string process_text(const std::string & text) {
// For now I skipped text romanization as I am unsure how to handle
// uroman and MeCab implementations in C++
// maybe something like https://github.com/anyascii/anyascii/ could work.
// currently only English would be supported in this function
std::string processed_text = replace_numbers_with_words(text);
std::transform(processed_text.begin(), processed_text.end(),
processed_text.begin(), ::tolower);
std::regex special_chars(R"([-_/,\.\\])");
processed_text = std::regex_replace(processed_text, special_chars, " ");
std::regex non_alpha(R"([^a-z\s])");
processed_text = std::regex_replace(processed_text, non_alpha, "");
std::regex multiple_spaces(R"(\s+)");
processed_text = std::regex_replace(processed_text, multiple_spaces, " ");
processed_text = std::regex_replace(processed_text, std::regex(R"(^\s+|\s+$)"), "");
/*
Replace spaces with the separator token same as in line 365
for (auto & c : prompt_user) {
if (c == ' ') {
prompt_clean += "<|text_sep|>";
*/
processed_text = std::regex_replace(processed_text, std::regex(R"(\s)"), "<|text_sep|>");
return processed_text;
}
static void prompt_add(llama_tokens & prompt, llama_token token) {
prompt.push_back(token);
}
static void prompt_add(llama_tokens & prompt, const llama_tokens & tokens) {
prompt.insert(prompt.end(), tokens.begin(), tokens.end());
}
static void prompt_add(llama_tokens & prompt, const llama_model * model, const std::string & txt, bool add_special, bool parse_special) {
auto tmp = common_tokenize(model, txt, add_special, parse_special);
prompt_add(prompt, tmp);
}
static void prompt_init(llama_tokens & prompt, const llama_model * model) {
prompt.clear();
prompt_add(prompt, model, "<|im_start|>\n", true, true);
}
int main(int argc, char ** argv) {
common_params params;
params.prompt = "";
params.n_predict = 4096;
params.n_batch = 8192;
params.n_ctx = 8192;
params.sampling.top_k = 4;
params.sampling.samplers = { COMMON_SAMPLER_TYPE_TOP_K, };
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_TTS, print_usage)) {
return 1;
}
const int n_parallel = params.n_parallel;
const int n_predict = params.n_predict;
common_init();
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model_ttc = NULL; // text-to-codes
llama_model * model_cts = NULL; // codes-to-speech
llama_context * ctx_ttc = NULL;
llama_context * ctx_cts = NULL;
common_init_result llama_init_ttc = common_init_from_params(params);
model_ttc = llama_init_ttc.model;
ctx_ttc = llama_init_ttc.context;
// TODO: refactor in a common struct
params.model = params.vocoder.model;
params.model_url = params.vocoder.model_url;
params.hf_repo = params.vocoder.hf_repo;
params.hf_file = params.vocoder.hf_file;
params.embedding = true;
common_init_result llama_init_cts = common_init_from_params(params);
model_cts = llama_init_cts.model;
ctx_cts = llama_init_cts.context;
std::vector<common_sampler *> smpl(n_parallel);
for (int i = 0; i < n_parallel; ++i) {
params.sampling.no_perf = (i != 0);
params.sampling.seed = params.sampling.seed + 1;
smpl[i] = common_sampler_init(model_ttc, params.sampling);
}
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl[0]));
LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str());
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl[0]).c_str());
LOG_INF("%s: loading done\n", __func__);
const auto t_main_start = ggml_time_us();
std::vector<llama_token> codes;
// process prompt and generate voice codes
{
LOG_INF("%s: constructing prompt ..\n", __func__);
std::vector<llama_token> prompt_inp;
prompt_init(prompt_inp, model_ttc);
prompt_add(prompt_inp, model_ttc, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true);
// convert the input text into the necessary format expected by OuteTTS
{
std::string prompt_clean = process_text(params.prompt);
LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
prompt_add(prompt_inp, model_ttc, prompt_clean, false, true);
}
prompt_add(prompt_inp, model_ttc, "<|text_end|>\n", false, true);
// disabled to save time on tokenizing each time
// TODO: load voices from the json files
#if 0
const std::string voice_data = R"(<|audio_start|>
the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|>
overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|>
package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|>
from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|>
just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|>
two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|>
people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|>
is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|>
pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|>
remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|>
sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|>
i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|>
have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|>
some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|>
critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|>
about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|>
some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|>
of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|>
the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|>
gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|>
aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|>
but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|>
its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|>
still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|>
really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|>
enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|>
and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|>
it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|>
looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|>
lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>)";
auto tmp = common_tokenize(model_ttc, voice_data, false, true);
printf("\n\n");
for (int i = 0; i < tmp.size(); ++i) {
printf("%d, ", tmp[i]);
}
printf("\n\n");
#else
prompt_add(prompt_inp, llama_tokens {
151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585,
152460, 153375, 151670, 198, 74455, 155808, 151669, 151799,
151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470,
151970, 153413, 152419, 153334, 153289, 153374, 153199, 152040,
153260, 152721, 152680, 153297, 152419, 153248, 152400, 152691,
153368, 153437, 151670, 198, 1722, 155828, 151669, 152607,
152256, 152991, 152299, 152688, 153163, 153016, 152789, 153198,
152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207,
152461, 153321, 153309, 151750, 152137, 153340, 152573, 152267,
153347, 151789, 152681, 153339, 151992, 152512, 151751, 152179,
153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904,
152311, 151670, 198, 1499, 155791, 151669, 152276, 152454,
153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226,
153043, 152325, 153267, 152622, 151670, 198, 4250, 155797,
151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271,
152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213,
152112, 153204, 151722, 152542, 151670, 198, 19789, 155796,
151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002,
152191, 151734, 152312, 152810, 152237, 153224, 153169, 153224,
152244, 153387, 153404, 151670, 198, 16069, 155811, 151669,
152265, 151946, 151808, 152412, 152363, 152305, 153156, 152733,
152810, 153157, 152016, 152100, 152069, 153234, 152317, 152589,
152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504,
153376, 152272, 152433, 152325, 151941, 151670, 198, 285,
155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381,
152474, 152680, 152157, 153255, 152324, 151682, 151670, 198,
32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682,
152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488,
153070, 151883, 152890, 152489, 153144, 153375, 152358, 151685,
152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669,
151902, 152720, 153377, 152027, 152378, 152821, 153207, 153459,
153028, 153068, 152507, 153255, 152158, 152921, 151958, 152609,
152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470,
152606, 152162, 152186, 153071, 152244, 153118, 153375, 153018,
152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736,
153380, 153502, 152702, 152115, 153181, 152735, 153277, 153457,
152393, 153112, 152595, 151670, 198, 19098, 155808, 151669,
152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239,
153163, 152922, 153402, 152034, 152591, 153438, 152215, 151673,
152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482,
152718, 152862, 153347, 151670, 198, 72, 155780, 151669, 151795,
152111, 152746, 152377, 153471, 152309, 151670, 198, 19016,
155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701,
152939, 152536, 152091, 151815, 152733, 151672, 151670, 198,
14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042,
153504, 152589, 153333, 151839, 151941, 153038, 153180, 151670,
198, 36996, 8303, 155832, 151669, 152231, 152256, 152835,
152801, 152985, 153400, 152393, 152818, 152765, 152249, 152600,
151699, 152302, 152752, 153018, 153009, 151992, 153054, 152847,
153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458,
152048, 152757, 152428, 153195, 151906, 153006, 153178, 153250,
152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418,
152228, 152733, 151670, 198, 9096, 155801, 151669, 151698,
153321, 152217, 153039, 152935, 153400, 152122, 152531, 153106,
152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851,
152901, 152885, 152594, 153446, 153080, 151670, 198, 14689,
155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191,
151673, 151690, 151698, 152714, 152846, 152981, 153171, 153384,
153364, 153188, 153246, 151670, 198, 1055, 155779, 151669,
151869, 152388, 152711, 153334, 151736, 151670, 198, 1782,
155780, 151669, 153483, 153240, 152241, 152558, 152697, 153046,
151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605,
153034, 153434, 153372, 153347, 151887, 152453, 152758, 152133,
152510, 152694, 152431, 152321, 153088, 152676, 152223, 152581,
152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032,
152903, 152859, 152989, 151748, 152669, 152661, 152650, 152409,
151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469,
152988, 152894, 151819, 152391, 153019, 152058, 153062, 153230,
151826, 152112, 152306, 152264, 152769, 153390, 152384, 152435,
152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540,
151919, 151893, 152558, 152817, 152946, 152956, 152129, 152715,
153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450,
151670, 198, 8088, 155792, 151669, 152452, 153497, 153353,
152679, 152533, 152382, 152374, 152611, 153341, 153163, 152285,
153411, 152495, 153141, 152320, 151670, 198, 1199, 155781,
151669, 151764, 152360, 153295, 152634, 153342, 152199, 152271,
151670, 198, 43366, 155799, 151669, 152308, 151682, 152889,
152016, 152385, 152629, 152495, 151826, 153321, 152958, 152180,
151886, 153432, 152922, 152128, 153024, 153040, 152593, 152287,
151677, 151670, 198, 53660, 155808, 151669, 151727, 152092,
152680, 153331, 151699, 152316, 152938, 152289, 152433, 153384,
151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691,
152489, 151941, 152049, 152034, 153053, 152179, 153160, 151676,
153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350,
152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234,
153135, 152291, 153235, 152143, 152583, 152402, 153483, 152678,
152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825,
152548, 153442, 152109, 152659, 153325, 152781, 152570, 152957,
151752, 152265, 153381, 152515, 151670, 198, 437, 155787,
151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174,
151792, 153409, 153327, 152990, 151670, 198, 275, 155781,
151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974,
151670, 198, 94273, 155799, 151669, 152953, 152938, 153427,
152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331,
152257, 152987, 152777, 153448, 152408, 151696, 152408, 152326,
152699, 151670, 198, 385, 16239, 155828, 151669, 152306, 152268,
153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110,
152918, 152923, 152467, 152331, 153053, 153330, 151889, 153444,
152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751,
152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499,
152109, 152255, 151739, 152267, 152759, 153318, 153165, 153349,
151670,});
#endif
// print the prompt token-by-token
LOG("\n");
for (auto id : prompt_inp) {
LOG("%s", common_token_to_piece(ctx_ttc, id).c_str());
}
LOG_INF("%s: prompt size: %d\n", __func__, (int) prompt_inp.size());
LOG("\n");
// create a llama_batch
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(std::max(prompt_inp.size(), (size_t) n_parallel), 0, n_parallel);
std::vector<llama_seq_id> seq_ids(n_parallel, 0);
for (int32_t i = 0; i < n_parallel; ++i) {
seq_ids[i] = i;
}
// evaluate the initial prompt
for (size_t i = 0; i < prompt_inp.size(); ++i) {
common_batch_add(batch, prompt_inp[i], i, seq_ids, false);
}
GGML_ASSERT(batch.n_tokens == (int) prompt_inp.size());
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx_ttc, batch) != 0) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
if (n_parallel > 1) {
LOG_INF("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
}
llama_synchronize(ctx_ttc);
LOG_INF("%s: time for prompt: %.3f ms\n\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
const auto t_dec_start = ggml_time_us();
// main loop
// 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_past = batch.n_tokens;
int n_decode = 0;
while (n_decode <= n_predict) {
// prepare the next batch
common_batch_clear(batch);
// 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;
}
const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
common_sampler_accept(smpl[i], new_token_id, true);
codes.push_back(new_token_id);
const auto * cands = common_sampler_get_candidates(smpl[i]);
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model_ttc, new_token_id) || n_decode == n_predict) {
std::string reason;
if (llama_token_is_eog(model_ttc, new_token_id)) {
reason = "eos";
} else {
reason = "n_predict";
}
i_batch[i] = -1;
LOG("\n");
if (n_parallel > 1) {
LOG_CNT("\n");
LOG_INF("%s: stream %d finished at n_past = %d, reason = '%s'\n", __func__, i, n_past, reason.c_str());
}
continue;
}
{
const float p = cands->data[cands->selected].p;
const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) ((3*p)*float(k_colors.size()))));
LOG_CNT("%s%d%s", k_colors[col].c_str(), i, "\033[0m");
//LOG_CNT("%d", i);
}
i_batch[i] = batch.n_tokens;
// push this new token for next evaluation
common_batch_add(batch, new_token_id, n_past, { i }, true);
}
// all streams are finished
if (batch.n_tokens == 0) {
break;
}
n_decode += 1;
n_past += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx_ttc, batch)) {
LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
llama_batch_free(batch);
LOG("\n");
LOG_INF("%s: time for decoder: %.3f ms\n", __func__, (ggml_time_us() - t_dec_start) / 1000.0f);
}
common_perf_print(ctx_ttc, smpl[0]);
//std::vector<llama_token> codes = {198, 88225, 155856, 151669, 152205,
// 153064, 152537, 153421, 153209, 152524, 151689, 152993, 152438, 152695,
// 153091, 152945, 152829, 152534, 152934, 153020, 151997, 152263, 153010,
// 153146, 152399, 153208, 152496, 151793, 152848, 152263, 152571, 153286,
// 152227, 153300, 152934, 152263, 153208, 152263, 152965, 152430, 152296,
// 153146, 152920, 152376, 152556, 153363, 151775, 152044, 152972, 152690,
// 153379, 152368, 152233, 153422, 152490, 151996, 152022, 151694, 152061,
// 153238, 152539, 153356, 152640, 153021, 153123, 151962, 153094, 151670,
// 198, 20339, 13189, 155824, 151669, 152070, 152007, 152910, 151683,
// 152000, 152373, 152760, 152046, 151735, 152334, 152394, 153073, 152908,
// 151856, 151953, 153247, 153293, 151903, 153480, 153168, 152478, 153359,
// 153429, 151905, 151678, 152567, 152411, 152165, 152556, 153075, 153424,
// 151993, 152999, 153078, 152151, 152088, 153389, 152484, 151874, 151670,
// 198, 285, 155784, 151669, 152226, 152126, 152638, 153215, 151729,
// 152959, 153479, 153059, 151838, 151670, 198, 1782, 155783, 151669,
// 153288, 153055, 153314, 152497, 152962, 152741, 152076, 153253, 151670,
// 198, 471, 16488, 155825, 151669, 152060, 152916, 151893, 153469, 152501,
// 152080, 152743, 151932, 153161, 152096, 152761, 152698, 153401, 153242,
// 153336, 152441, 152838, 153467, 152706, 153496, 153310, 152422, 153360,
// 153115, 152763, 151998, 152373, 153450, 152554, 151968, 153323, 152055,
// 152468, 153111, 153358, 152813, 152010, 151770, 152823, 152960, 151670,
// 198, 22627, 155823, 151669, 152814, 152366, 153484, 152931, 153441,
// 152164, 152877, 152915, 153463, 151692, 152911, 152747, 152776, 151831,
// 153449, 151882, 152975, 152031, 152513, 153150, 152448, 152667, 153133,
// 153189, 152619, 153466, 152054, 152106, 153119, 152277, 152439, 153109,
// 152997, 152141, 153154, 153256, 153311, 151922, 151670, 198, 1055,
// 155781, 151669, 152633, 151850, 153060, 153270, 152560, 153348, 152729,
// 151670, 198, 25312, 155803, 151669, 152521, 153403, 152561, 153337,
// 153383, 152199, 153493, 153326, 151830, 152254, 152248, 152349, 152153,
// 153007, 151823, 153037, 152575, 152457, 152406, 152592, 153116, 153365,
// 153456, 151670, 198, 88225, 155817, 151669, 153271, 151925, 152218,
// 152418, 152253, 153140, 151903, 153151, 152626, 152338, 152647, 153464,
// 152785, 152768, 151711, 152037, 152033, 151804, 152216, 151701, 151855,
// 152348, 152995, 152955, 152905, 152342, 152340, 153391, 153453, 152418,
// 153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645};
{
const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
LOG("\n");
LOG_INF("codes: '%s'\n", inp_txt.c_str());
LOG_INF("%s: codes size: %d\n", __func__, (int) codes.size());
}
// remove all non-audio tokens (i.e. < 151672 || > 155772)
codes.erase(std::remove_if(codes.begin(), codes.end(), [](llama_token t) { return t < 151672 || t > 155772; }), codes.end());
{
const std::string inp_txt = common_detokenize(ctx_ttc, codes, true);
LOG_INF("codes audio: '%s'\n", inp_txt.c_str());
LOG_INF("%s: codes audio size: %d\n", __func__, (int) codes.size());
}
for (auto & token : codes) {
token -= 151672;
}
const auto t_voc_start = ggml_time_us();
const int n_codes = codes.size();
llama_batch batch = llama_batch_init(n_codes, 0, 1);
for (size_t i = 0; i < codes.size(); ++i) {
common_batch_add(batch, codes[i], i, { 0 }, true); // TODO: all logits?
}
GGML_ASSERT(batch.n_tokens == n_codes);
if (llama_decode(ctx_cts, batch) != 0) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
llama_synchronize(ctx_cts);
LOG_INF("%s: time for vocoder: %.3f ms\n", __func__, (ggml_time_us() - t_voc_start) / 1000.0f);
const auto t_spec_start = ggml_time_us();
#if 1
// spectral operations
const int n_embd = llama_n_embd(model_cts);
const float * embd = llama_get_embeddings(ctx_cts);
auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads);
#else
// read the spectrogram from a file for debugging purposes
std::vector<float> audio;
{
std::ifstream fin("out.bin", std::ios::binary);
if (!fin) {
LOG_ERR("%s: failed to open file '%s'\n", __func__, "out.bin");
return 1;
}
std::vector<float> embd;
int n_codes;
int n_embd;
fin.read(reinterpret_cast<char *>(&n_codes), sizeof(int));
fin.read(reinterpret_cast<char *>(&n_embd), sizeof(int));
embd.resize(n_codes * n_embd);
fin.read(reinterpret_cast<char *>(embd.data()), n_codes * n_embd * sizeof(float));
fin.close();
LOG_INF("%s: n_codes: %d, n_embd: %d\n", __func__, n_codes, n_embd);
audio = embd_to_audio(embd.data(), n_codes, n_embd, params.cpuparams.n_threads);
}
#endif
const std::string fname = "output.wav";
const int n_sr = 24000; // sampling rate
// zero out first 0.25 seconds
for (int i = 0; i < 24000/4; ++i) {
audio[i] = 0.0f;
}
LOG_INF("%s: time for spectral ops: %.3f ms\n", __func__, (ggml_time_us() - t_spec_start) / 1000.0f);
LOG_INF("%s: total time: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f);
save_wav16(fname, audio, n_sr);
LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str());
llama_free(ctx_ttc);
llama_free_model(model_ttc);
llama_free(ctx_cts);
llama_free_model(model_cts);
llama_backend_free();
return 0;
}

11
ggml/CMakeLists.txt
View file

@ -74,10 +74,10 @@ if (NOT GGML_CUDA_GRAPHS_DEFAULT)
endif()
# general
option(GGML_STATIC "ggml: static link libraries" OFF)
option(GGML_NATIVE "ggml: enable -march=native flag" ${GGML_NATIVE_DEFAULT})
option(GGML_LTO "ggml: enable link time optimization" OFF)
option(GGML_CCACHE "ggml: use ccache if available" ON)
option(GGML_STATIC "ggml: static link libraries" OFF)
option(GGML_NATIVE "ggml: optimize the build for the current system" ${GGML_NATIVE_DEFAULT})
option(GGML_LTO "ggml: enable link time optimization" OFF)
option(GGML_CCACHE "ggml: use ccache if available" ON)
# debug
option(GGML_ALL_WARNINGS "ggml: enable all compiler warnings" ON)
@ -120,8 +120,9 @@ endif()
option(GGML_LASX "ggml: enable lasx" ON)
option(GGML_LSX "ggml: enable lsx" ON)
option(GGML_RVV "ggml: enable rvv" ON)
option(GGML_SVE "ggml: enable SVE" OFF)
option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM")
if (WIN32)

41
ggml/include/ggml.h
View file

@ -1564,17 +1564,6 @@ extern "C" {
int d1, // dilation dimension 1
bool is_2D);
GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1); // dilation dimension 1
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
@ -1592,6 +1581,23 @@ extern "C" {
int s, // stride
int d); // dilation
// depthwise
// TODO: this is very likely wrong for some cases! - needs more testing
GGML_API struct ggml_tensor * ggml_conv_1d_dw(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride
int p0, // padding
int d0); // dilation
GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride
int d0); // dilation
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
@ -1611,7 +1617,6 @@ extern "C" {
int d0, // dilation dimension 0
int d1); // dilation dimension 1
// kernel size is a->ne[0] x a->ne[1]
// stride is equal to kernel size
// padding is zero
@ -1638,6 +1643,18 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// depthwise
GGML_API struct ggml_tensor * ggml_conv_2d_dw(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1); // dilation dimension 1
GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,

1
ggml/src/ggml-alloc.c
View file

@ -534,7 +534,6 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
hn->buffer_id = buffer_id;
hn->offset = offset;
return;
}
}

147
ggml/src/ggml-cpu/CMakeLists.txt
View file

@ -74,112 +74,77 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR
CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
(NOT CMAKE_OSX_ARCHITECTURES AND
NOT CMAKE_GENERATOR_PLATFORM_LWR AND
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
message(STATUS "ARM detected")
if (MSVC)
list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead
list(APPEND ARCH_DEFINITIONS __ARM_NEON)
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA)
set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
if (GGML_COMPILER_SUPPORT_DOTPROD)
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
message(STATUS "ARM feature DOTPROD enabled")
endif ()
check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
message(STATUS "ARM feature MATMUL_INT8 enabled")
endif ()
check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled")
endif ()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
elseif (APPLE)
if (GGML_NATIVE)
set(USER_PROVIDED_MARCH FALSE)
foreach(flag_var IN ITEMS CMAKE_C_FLAGS CMAKE_CXX_FLAGS CMAKE_REQUIRED_FLAGS)
if ("${${flag_var}}" MATCHES "-march=[a-zA-Z0-9+._-]+")
set(USER_PROVIDED_MARCH TRUE)
break()
endif()
endforeach()
if (NOT USER_PROVIDED_MARCH)
set(MARCH_FLAGS "-march=armv8.2a")
check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
if (GGML_COMPILER_SUPPORT_DOTPROD)
set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod")
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD)
message(STATUS "ARM feature DOTPROD enabled")
endif ()
set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm")
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}")
check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm")
list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8)
message(STATUS "ARM feature MATMUL_INT8 enabled")
endif ()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
list(APPEND ARCH_FLAGS "${MARCH_FLAGS}")
endif ()
endif ()
if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
else()
check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
list(APPEND ARCH_FLAGS -mfp16-format=ieee)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
# Raspberry Pi 1, Zero
list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
# Android armeabi-v7a
list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
else()
# Raspberry Pi 2
list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
if (GGML_NATIVE)
list(APPEND ARCH_FLAGS -mcpu=native)
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
# -mcpu=native does not always enable all the features in some compilers,
# so we check for them manually and enable them if available
include(CheckCXXSourceRuns)
set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod")
check_cxx_source_runs(
"#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }"
GGML_COMPILER_SUPPORT_DOTPROD)
if (GGML_COMPILER_SUPPORT_DOTPROD)
set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod")
endif()
set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm")
check_cxx_source_runs(
"#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }"
GGML_COMPILER_SUPPORT_I8MM)
if (GGML_COMPILER_SUPPORT_I8MM)
set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm")
endif()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
else()
if (GGML_CPU_ARM_ARCH)
list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
endif()
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
# Android arm64-v8a
# Raspberry Pi 3, 4, Zero 2 (32-bit)
list(APPEND ARCH_FLAGS -mno-unaligned-access)
endif()
if (GGML_SVE)
list(APPEND ARCH_FLAGS -march=armv8.6-a+sve)
# show enabled features
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
INPUT_FILE "/dev/null"
OUTPUT_VARIABLE ARM_FEATURE
RESULT_VARIABLE ARM_FEATURE_RESULT
)
if (ARM_FEATURE_RESULT)
message(FATAL_ERROR "Failed to get ARM features")
else()
foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
if (NOT ${feature_pos} EQUAL -1)
message(STATUS "ARM feature ${feature} enabled")
endif()
endforeach()
endif()
endif()
elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
message(STATUS "x86 detected")
if (MSVC)
# instruction set detection for MSVC only
if (GGML_NATIVE)

9
ggml/src/ggml-cpu/ggml-cpu.cpp
View file

@ -394,8 +394,11 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
switch (op->op) {
case GGML_OP_CPY:
return
op->type != GGML_TYPE_IQ3_XXS &&
op->type != GGML_TYPE_IQ3_S &&
op->type != GGML_TYPE_IQ2_XXS &&
op->type != GGML_TYPE_IQ2_XS &&
op->type != GGML_TYPE_IQ2_S &&
op->type != GGML_TYPE_IQ1_S &&
op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
case GGML_OP_MUL_MAT:
@ -519,6 +522,12 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
if (ggml_cpu_has_sve()) {
features.push_back({ "SVE", "1" });
}
if (ggml_cpu_has_dotprod()) {
features.push_back({ "DOTPROD", "1" });
}
if (ggml_cpu_has_matmul_int8()) {
features.push_back({ "MATMUL_INT8", "1" });
}
if (ggml_cpu_get_sve_cnt() > 0) {
static std::string sve_cnt = std::to_string(ggml_cpu_get_sve_cnt());
features.push_back({ "SVE_CNT", sve_cnt.c_str() });

1
ggml/src/ggml-cpu/llamafile/sgemm.cpp
View file

@ -204,6 +204,7 @@ template <> inline float32x4_t load(const float *p) {
return vld1q_f32(p);
}
#if !defined(_MSC_VER)
// FIXME: this should check for __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
template <> inline float16x8_t load(const ggml_fp16_t *p) {
return vld1q_f16((const float16_t *)p);
}

16
ggml/src/ggml-impl.h
View file

@ -551,6 +551,22 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
// expose GGUF internals for test code
GGML_API size_t gguf_type_size(enum gguf_type type);
GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
struct gguf_buf {
void * data;
size_t size;
size_t offset;
};
GGML_API struct gguf_buf gguf_buf_init(size_t size);
GGML_API void gguf_buf_free(struct gguf_buf buf);
GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta);
#ifdef __cplusplus
}
#endif

170
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -245,6 +245,7 @@ struct vk_device_struct {
vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
vk_pipeline pipeline_timestep_embedding_f32;
vk_pipeline pipeline_pool2d_f32;
vk_pipeline pipeline_rwkv_wkv6_f32;
// [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
@ -528,6 +529,13 @@ struct vk_op_pool2d_push_constants {
int32_t p0; int32_t p1;
};
struct vk_op_rwkv_wkv6_push_constants {
uint32_t B;
uint32_t T;
uint32_t C;
uint32_t H;
};
// Allow pre-recording command buffers
struct vk_staging_memcpy {
vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@ -1363,7 +1371,7 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
// Needs to be kept up to date on shader changes
const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
const uint32_t warps = warptile[0] / device->subgroup_size;
const uint32_t warps = warptile[0] / warptile[10];
const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size;
const uint32_t mmid_row_ids = mul_mat_id ? 3072 * sizeof(uint32_t) : 0;
@ -1377,8 +1385,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
std::cerr << "ggml_vulkan: Compiling shaders";
// some shaders require the subgroup size to be 16 or larger
// some shaders have a minimum subgroup size
const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);
// mulmat
std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
@ -1445,7 +1454,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, tm_l, tn_l, tk_l, device->subgroup_size };
m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, tm_m, tn_m, tk_m, device->subgroup_size };
s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
s_warptile_mmq = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 };
@ -1864,7 +1873,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@ -1878,7 +1887,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@ -1892,7 +1901,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
// dequant shaders
ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@ -2014,6 +2023,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
for (auto &c : compiles) {
c.wait();
}
@ -5022,6 +5033,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_pool2d_f32;
}
return nullptr;
case GGML_OP_RWKV_WKV6:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_rwkv_wkv6_f32;
}
return nullptr;
case GGML_OP_LEAKY_RELU:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_leaky_relu_f32;
@ -5424,6 +5440,134 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
}, dryrun);
}
static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, bool dryrun = false) {
const ggml_tensor * k = dst->src[0];
const ggml_tensor * v = dst->src[1];
const ggml_tensor * r = dst->src[2];
const ggml_tensor * tf = dst->src[3];
const ggml_tensor * td = dst->src[4];
const ggml_tensor * state = dst->src[5];
GGML_ASSERT(!ggml_is_quantized(k->type));
GGML_ASSERT(!ggml_is_quantized(v->type));
GGML_ASSERT(!ggml_is_quantized(r->type));
GGML_ASSERT(!ggml_is_quantized(tf->type));
GGML_ASSERT(!ggml_is_quantized(td->type));
GGML_ASSERT(!ggml_is_quantized(state->type));
GGML_ASSERT(dst->buffer != nullptr);
vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, k, v, r, dst, GGML_OP_RWKV_WKV6);
GGML_ASSERT(pipeline != nullptr);
if (dryrun) {
ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
return;
}
ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context;
ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context;
ggml_backend_vk_buffer_context * r_buf_ctx = (ggml_backend_vk_buffer_context *)r->buffer->context;
ggml_backend_vk_buffer_context * tf_buf_ctx = (ggml_backend_vk_buffer_context *)tf->buffer->context;
ggml_backend_vk_buffer_context * td_buf_ctx = (ggml_backend_vk_buffer_context *)td->buffer->context;
ggml_backend_vk_buffer_context * state_buf_ctx = (ggml_backend_vk_buffer_context *)state->buffer->context;
ggml_vk_sync_buffers(subctx);
vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State;
uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset;
bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false;
if (ctx->device->uma) {
ggml_vk_host_get(ctx->device, k->data, d_K, k_offset);
ggml_vk_host_get(ctx->device, v->data, d_V, v_offset);
ggml_vk_host_get(ctx->device, r->data, d_R, r_offset);
ggml_vk_host_get(ctx->device, tf->data, d_TF, tf_offset);
ggml_vk_host_get(ctx->device, td->data, d_TD, td_offset);
ggml_vk_host_get(ctx->device, state->data, d_State, state_offset);
ggml_vk_host_get(ctx->device, dst->data, d_D, dst_offset);
K_uma = d_K != nullptr;
V_uma = d_V != nullptr;
R_uma = d_R != nullptr;
TF_uma = d_TF != nullptr;
TD_uma = d_TD != nullptr;
STATE_uma = d_State != nullptr;
DST_uma = d_D != nullptr;
}
if (!K_uma) {
d_K = k_buf_ctx->dev_buffer;
k_offset = vk_tensor_offset(k) + k->view_offs;
}
if (!V_uma) {
d_V = v_buf_ctx->dev_buffer;
v_offset = vk_tensor_offset(v) + v->view_offs;
}
if (!R_uma) {
d_R = r_buf_ctx->dev_buffer;
r_offset = vk_tensor_offset(r) + r->view_offs;
}
if (!TF_uma) {
d_TF = tf_buf_ctx->dev_buffer;
tf_offset = vk_tensor_offset(tf) + tf->view_offs;
}
if (!TD_uma) {
d_TD = td_buf_ctx->dev_buffer;
td_offset = vk_tensor_offset(td) + td->view_offs;
}
if (!STATE_uma) {
d_State = state_buf_ctx->dev_buffer;
state_offset = vk_tensor_offset(state) + state->view_offs;
}
if (!DST_uma) {
d_D = dst_buf_ctx->dev_buffer;
dst_offset = vk_tensor_offset(dst) + dst->view_offs;
}
const uint64_t k_size = ggml_nbytes(k);
const uint64_t v_size = ggml_nbytes(v);
const uint64_t r_size = ggml_nbytes(r);
const uint64_t tf_size = ggml_nbytes(tf);
const uint64_t td_size = ggml_nbytes(td);
const uint64_t state_size = ggml_nbytes(state);
const uint64_t dst_size = ggml_nbytes(dst);
std::array<uint32_t, 3> elements = {
(uint32_t)(pc.B * pc.H),
1,
1
};
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {
vk_subbuffer{ d_K, k_offset, k_size },
vk_subbuffer{ d_V, v_offset, v_size },
vk_subbuffer{ d_R, r_offset, r_size },
vk_subbuffer{ d_TF, tf_offset, tf_size },
vk_subbuffer{ d_TD, td_offset, td_size },
vk_subbuffer{ d_State, state_offset, state_size },
vk_subbuffer{ d_D, dst_offset, dst_size }
}, sizeof(vk_op_rwkv_wkv6_push_constants), &pc, elements);
}
static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) {
const size_t seq_length = dst->src[0]->ne[3];
const size_t n_embed = dst->ne[0];
const size_t n_heads = dst->src[0]->ne[2];
const size_t n_seqs = dst->src[5]->ne[1];
ggml_vk_op_f32_rwkv6(
ctx, subctx, dst,
{
(uint32_t)n_seqs,
(uint32_t)seq_length,
(uint32_t)n_embed,
(uint32_t)n_heads,
},
dryrun
);
}
static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
int * op_params = (int *)dst->op_params;
@ -6569,6 +6713,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
case GGML_OP_FLASH_ATTN_EXT:
break;
@ -6768,6 +6913,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_FLASH_ATTN_EXT:
ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
break;
case GGML_OP_RWKV_WKV6:
ggml_vk_rwkv_wkv6(ctx, compute_ctx, node, dryrun);
break;
default:
return false;
@ -6848,6 +6998,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
case GGML_OP_REPEAT:
buf = tensor->buffer;
@ -7724,6 +7875,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
return true;
default:
@ -8300,7 +8452,11 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
} else if (tensor->op == GGML_OP_LEAKY_RELU) {
const float * op_params = (const float *)tensor->op_params;
tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);
} else {
} else if (tensor->op == GGML_OP_RWKV_WKV6) {
tensor_clone = ggml_rwkv_wkv6(ggml_ctx, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3],
tensor->src[4], tensor->src[5]);
}
else {
std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
GGML_ABORT("fatal error");
}

2
ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
View file

@ -32,7 +32,7 @@ shared FLOAT_TYPE vals[BLOCK_SIZE];
void soft_max(uint num_iters) {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
const uint rowy = rowx % p.KY;
const uint rowy = (p.KY > 0) ? (rowx % p.KY) : 0;
if (rowx >= p.nrows_x) {
return;

2
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View file

@ -479,6 +479,8 @@ void process_shaders() {
string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
for (auto &c : compiles) {
c.wait();
}

87
ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp Normal file
View file

@ -0,0 +1,87 @@
#version 450
#extension GL_EXT_control_flow_attributes : require
#define BLOCK_SIZE 64
layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
layout(push_constant) uniform Parameters {
uint B;
uint T;
uint C;
uint H;
};
layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
layout(binding = 2) readonly buffer RBuf { A_TYPE r[]; };
layout(binding = 3) readonly buffer TimeFBuf { A_TYPE tf[]; };
layout(binding = 4) readonly buffer TimeDBuf { A_TYPE td[]; };
layout(binding = 5) readonly buffer StateBuf { A_TYPE state_in[]; };
layout(binding = 6) buffer DstBuf { A_TYPE dst[]; };
shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE];
void main() {
const uint head_size = BLOCK_SIZE;
const uint batch_id = gl_WorkGroupID.x / H;
const uint head_id = gl_WorkGroupID.x % H;
const uint tid = gl_LocalInvocationID.x;
const uint state_size = C * head_size;
const uint n_seq_tokens = T / B;
if (batch_id >= B || head_id >= H) {
return;
}
A_TYPE state[BLOCK_SIZE];
[[unroll]] for (uint i = 0; i < head_size; i++) {
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid];
}
barrier();
_tf[tid] = tf[head_id * head_size + tid];
barrier();
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
for (uint t = start_t; t < end_t; t += C) {
barrier();
_k[tid] = k[t];
_r[tid] = r[t];
_td[tid] = td[t];
barrier();
const A_TYPE v_val = v[t];
A_TYPE y = 0.0;
[[unroll]] for (uint j = 0; j < head_size; j += 4) {
vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
vec4 tf_vec = vec4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
vec4 td_vec = vec4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]);
vec4 kv = k_vec * v_val;
vec4 temp = tf_vec * kv + s_vec;
y += dot(r_vec, temp);
s_vec = s_vec * td_vec + kv;
state[j] = s_vec.x;
state[j+1] = s_vec.y;
state[j+2] = s_vec.z;
state[j+3] = s_vec.w;
}
dst[t] = y;
}
[[unroll]] for (uint i = 0; i < head_size; i++) {
dst[T * C + batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid] = state[i];
}
}

295
ggml/src/ggml.c
View file

@ -3760,104 +3760,10 @@ struct ggml_tensor * ggml_clamp(
return result;
}
// ggml_conv_1d
static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
}
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0) {
struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
struct ggml_tensor * result =
ggml_mul_mat(ctx,
ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K]
result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
return result;
}
// ggml_conv_1d_ph
struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d) {
return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
}
// ggml_conv_transpose_1d
static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
}
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0) {
GGML_ASSERT(ggml_is_matrix(b));
GGML_ASSERT(a->ne[2] == b->ne[1]);
GGML_ASSERT(a->ne[3] == 1);
GGML_ASSERT(p0 == 0);
GGML_ASSERT(d0 == 1);
const int64_t ne[4] = {
ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
a->ne[1], b->ne[2], 1,
};
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { s0, p0, d0 };
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_TRANSPOSE_1D;
result->src[0] = a;
result->src[1] = b;
return result;
}
// ggml_conv_depthwise
struct ggml_tensor * ggml_conv_depthwise_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1) {
struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
return result;
}
// ggml_conv_2d
// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
// a: [OC，IC, KH, KW]
// b: [N, IC, IH, IW]
@ -3874,10 +3780,11 @@ struct ggml_tensor * ggml_im2col(
int d1,
bool is_2D,
enum ggml_type dst_type) {
if(is_2D) {
if (is_2D) {
GGML_ASSERT(a->ne[2] == b->ne[2]);
} else {
GGML_ASSERT(a->ne[1] == b->ne[1]);
//GGML_ASSERT(b->ne[1] % a->ne[1] == 0);
GGML_ASSERT(b->ne[1] == a->ne[1]);
GGML_ASSERT(b->ne[3] == 1);
}
@ -3928,6 +3835,108 @@ struct ggml_tensor * ggml_im2col_back(
return result;
}
// ggml_conv_1d
struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0) {
struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
struct ggml_tensor * result =
ggml_mul_mat(ctx,
ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K]
ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K]
result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL]
return result;
}
// ggml_conv_1d_ph
struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d) {
return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
}
// ggml_conv_1d_dw
struct ggml_tensor * ggml_conv_1d_dw(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0) {
struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]);
struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]);
struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16);
struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a);
result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1);
return result;
}
// ggml_conv_1d_dw_ph
struct ggml_tensor * ggml_conv_1d_dw_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int d0) {
return ggml_conv_1d_dw(ctx, a, b, s0, a->ne[0] / 2, d0);
}
// ggml_conv_transpose_1d
static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
return (ins - 1) * s - 2 * p + d * (ks - 1) + 1;
}
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0) {
GGML_ASSERT(ggml_is_matrix(b));
GGML_ASSERT(a->ne[2] == b->ne[1]);
GGML_ASSERT(a->ne[3] == 1);
GGML_ASSERT(p0 == 0);
GGML_ASSERT(d0 == 1);
const int64_t ne[4] = {
ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/),
a->ne[1], b->ne[2], 1,
};
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { s0, p0, d0 };
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_TRANSPOSE_1D;
result->src[0] = a;
result->src[1] = b;
return result;
}
// ggml_conv_2d
// a: [OC，IC, KH, KW]
// b: [N, IC, IH, IW]
// result: [N, OC, OH, OW]
@ -3973,6 +3982,31 @@ struct ggml_tensor * ggml_conv_2d_s1_ph(
return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1);
}
// ggml_conv_2d_dw
struct ggml_tensor * ggml_conv_2d_dw(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1) {
struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
return result;
}
// ggml_conv_transpose_2d_p0
static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
@ -6037,12 +6071,12 @@ struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, co
struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL;
return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grads ? cgraph->grads[igrad] : NULL;
}
struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL;
return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grad_accs ? cgraph->grad_accs[igrad] : NULL;
}
void ggml_graph_print(const struct ggml_cgraph * cgraph) {
@ -6489,7 +6523,7 @@ struct gguf_context {
void * data;
};
static size_t gguf_type_size(enum gguf_type type) {
size_t gguf_type_size(enum gguf_type type) {
GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT);
return GGUF_TYPE_SIZE[type];
}
@ -6617,13 +6651,7 @@ struct gguf_context * gguf_init_empty(void) {
return ctx;
}
struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
FILE * file = ggml_fopen(fname, "rb");
if (!file) {
fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
return NULL;
}
struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
// offset from start of file
size_t offset = 0;
@ -6636,7 +6664,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
for (uint32_t i = 0; i < sizeof(magic); i++) {
if (magic[i] != GGUF_MAGIC[i]) {
fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
fclose(file);
return NULL;
}
}
@ -6647,7 +6674,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
if (!ctx) {
fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
fclose(file);
return NULL;
}
@ -6665,7 +6691,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (ctx->header.version == 1) {
fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6678,7 +6703,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (!ok) {
fprintf(stderr, "%s: failed to read header\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6688,12 +6712,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
{
const uint64_t n_kv = ctx->header.n_kv;
ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
if (!ctx->kv) {
fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
if (n_kv > 0) {
ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
if (!ctx->kv) {
fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
gguf_free(ctx);
return NULL;
}
}
for (uint64_t i = 0; i < n_kv; ++i) {
@ -6740,7 +6765,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// prevent from integer overflow in the malloc below
if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) {
fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6748,7 +6772,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
if (!kv->value.arr.data) {
fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6760,7 +6783,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// prevent from integer overflow in the malloc below
if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) {
fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6768,7 +6790,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str));
if (!kv->value.arr.data) {
fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6799,7 +6820,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (!ok) {
fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6810,7 +6830,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
if (!ctx->infos) {
fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6846,7 +6865,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (!ok) {
fprintf(stderr, "%s: failed to read tensor info\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6889,7 +6907,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// this tensor type support have been removed:
fprintf(stderr, "%s: tensor '%s' of type %d: %s\n",
__func__, info->name.data, (int) info->type, ggml_type_name(info->type));
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6897,7 +6914,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (ne % ggml_blck_size(info->type) != 0) {
fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
__func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6929,7 +6945,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
*params.ctx = ggml_init(pdata);
if (*params.ctx == NULL) {
fprintf(stderr, "%s: failed to initialize context\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
@ -6948,7 +6963,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (!ok) {
fprintf(stderr, "%s: failed to read tensor data\n", __func__);
fclose(file);
ggml_free(ctx_data);
gguf_free(ctx);
return NULL;
@ -6987,7 +7001,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
if (!ok) {
fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
fclose(file);
ggml_free(ctx_data);
gguf_free(ctx);
return NULL;
@ -6996,11 +7009,21 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
ggml_set_no_alloc(ctx_data, params.no_alloc);
}
fclose(file);
return ctx;
}
struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
FILE * file = ggml_fopen(fname, "rb");
if (!file) {
fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
return NULL;
}
struct gguf_context * result = gguf_init_from_file_impl(file, params);
fclose(file);
return result;
}
void gguf_free(struct gguf_context * ctx) {
if (ctx == NULL) {
return;
@ -7460,13 +7483,7 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo
// fwrite(val, sizeof(char), size, file);
//}
struct gguf_buf {
void * data;
size_t size;
size_t offset;
};
static struct gguf_buf gguf_buf_init(size_t size) {
struct gguf_buf gguf_buf_init(size_t size) {
struct gguf_buf buf = {
/*buf.data =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
/*buf.size =*/ size,
@ -7476,7 +7493,7 @@ static struct gguf_buf gguf_buf_init(size_t size) {
return buf;
}
static void gguf_buf_free(struct gguf_buf buf) {
void gguf_buf_free(struct gguf_buf buf) {
if (buf.data) {
GGML_FREE(buf.data);
}
@ -7514,7 +7531,7 @@ static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_si
buf->offset += el_size;
}
static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
// write header
gguf_bwrite_el(buf, &ctx->header.magic, sizeof(ctx->header.magic));
gguf_bwrite_el(buf, &ctx->header.version, sizeof(ctx->header.version));

275
gguf-py/gguf/constants.py
View file

@ -90,6 +90,7 @@ class Keys:
VOCAB_SIZE = "{arch}.vocab_size"
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
FEATURES_LENGTH = "{arch}.features_length"
BLOCK_COUNT = "{arch}.block_count"
LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
@ -122,6 +123,8 @@ class Keys:
VALUE_LENGTH = "{arch}.attention.value_length"
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
GROUPNORM_EPS = "{arch}.attention.group_norm_epsilon"
GROUPNORM_GROUPS = "{arch}.attention.group_norm_groups"
CAUSAL = "{arch}.attention.causal"
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
@ -155,6 +158,14 @@ class Keys:
class WKV:
HEAD_SIZE = "{arch}.wkv.head_size"
class PosNet:
EMBEDDING_LENGTH = "{arch}.posnet.embedding_length"
BLOCK_COUNT = "{arch}.posnet.block_count"
class ConvNext:
EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
BLOCK_COUNT = "{arch}.convnext.block_count"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
PRE = "tokenizer.ggml.pre"
@ -209,58 +220,59 @@ class GGUFType:
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
BAICHUAN = auto()
GROK = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
STARCODER = auto()
REFACT = auto()
BERT = auto()
NOMIC_BERT = auto()
JINA_BERT_V2 = auto()
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
QWEN2 = auto()
QWEN2MOE = auto()
QWEN2VL = auto()
PHI2 = auto()
PHI3 = auto()
PLAMO = auto()
CODESHELL = auto()
ORION = auto()
INTERNLM2 = auto()
MINICPM = auto()
MINICPM3 = auto()
GEMMA = auto()
GEMMA2 = auto()
STARCODER2 = auto()
RWKV6 = auto()
MAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
DBRX = auto()
OLMO = auto()
OLMO2 = auto()
OLMOE = auto()
OPENELM = auto()
ARCTIC = auto()
DEEPSEEK = auto()
DEEPSEEK2 = auto()
CHATGLM = auto()
BITNET = auto()
T5 = auto()
T5ENCODER = auto()
JAIS = auto()
NEMOTRON = auto()
EXAONE = auto()
GRANITE = auto()
GRANITE_MOE = auto()
CHAMELEON = auto()
LLAMA = auto()
FALCON = auto()
BAICHUAN = auto()
GROK = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
STARCODER = auto()
REFACT = auto()
BERT = auto()
NOMIC_BERT = auto()
JINA_BERT_V2 = auto()
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
QWEN2 = auto()
QWEN2MOE = auto()
QWEN2VL = auto()
PHI2 = auto()
PHI3 = auto()
PLAMO = auto()
CODESHELL = auto()
ORION = auto()
INTERNLM2 = auto()
MINICPM = auto()
MINICPM3 = auto()
GEMMA = auto()
GEMMA2 = auto()
STARCODER2 = auto()
RWKV6 = auto()
MAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
DBRX = auto()
OLMO = auto()
OLMO2 = auto()
OLMOE = auto()
OPENELM = auto()
ARCTIC = auto()
DEEPSEEK = auto()
DEEPSEEK2 = auto()
CHATGLM = auto()
BITNET = auto()
T5 = auto()
T5ENCODER = auto()
JAIS = auto()
NEMOTRON = auto()
EXAONE = auto()
GRANITE = auto()
GRANITE_MOE = auto()
CHAMELEON = auto()
WAVTOKENIZER_DEC = auto()
class MODEL_TENSOR(IntEnum):
@ -370,61 +382,78 @@ class MODEL_TENSOR(IntEnum):
ENC_OUTPUT_NORM = auto()
CLS = auto() # classifier
CLS_OUT = auto() # classifier output projection
CONV1D = auto()
CONVNEXT_DW = auto()
CONVNEXT_NORM = auto()
CONVNEXT_PW1 = auto()
CONVNEXT_PW2 = auto()
CONVNEXT_GAMMA = auto()
POSNET_CONV1 = auto()
POSNET_CONV2 = auto()
POSNET_NORM = auto()
POSNET_NORM1 = auto()
POSNET_NORM2 = auto()
POSNET_ATTN_NORM = auto()
POSNET_ATTN_Q = auto()
POSNET_ATTN_K = auto()
POSNET_ATTN_V = auto()
POSNET_ATTN_OUT = auto()
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.BAICHUAN: "baichuan",
MODEL_ARCH.GROK: "grok",
MODEL_ARCH.GPT2: "gpt2",
MODEL_ARCH.GPTJ: "gptj",
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
MODEL_ARCH.STARCODER: "starcoder",
MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.NOMIC_BERT: "nomic-bert",
MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
MODEL_ARCH.QWEN2: "qwen2",
MODEL_ARCH.QWEN2MOE: "qwen2moe",
MODEL_ARCH.QWEN2VL: "qwen2vl",
MODEL_ARCH.PHI2: "phi2",
MODEL_ARCH.PHI3: "phi3",
MODEL_ARCH.PLAMO: "plamo",
MODEL_ARCH.CODESHELL: "codeshell",
MODEL_ARCH.ORION: "orion",
MODEL_ARCH.INTERNLM2: "internlm2",
MODEL_ARCH.MINICPM: "minicpm",
MODEL_ARCH.MINICPM3: "minicpm3",
MODEL_ARCH.GEMMA: "gemma",
MODEL_ARCH.GEMMA2: "gemma2",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.RWKV6: "rwkv6",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.OLMO2: "olmo2",
MODEL_ARCH.OLMOE: "olmoe",
MODEL_ARCH.OPENELM: "openelm",
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK: "deepseek",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.CHATGLM: "chatglm",
MODEL_ARCH.BITNET: "bitnet",
MODEL_ARCH.T5: "t5",
MODEL_ARCH.T5ENCODER: "t5encoder",
MODEL_ARCH.JAIS: "jais",
MODEL_ARCH.NEMOTRON: "nemotron",
MODEL_ARCH.EXAONE: "exaone",
MODEL_ARCH.GRANITE: "granite",
MODEL_ARCH.GRANITE_MOE: "granitemoe",
MODEL_ARCH.CHAMELEON: "chameleon",
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.BAICHUAN: "baichuan",
MODEL_ARCH.GROK: "grok",
MODEL_ARCH.GPT2: "gpt2",
MODEL_ARCH.GPTJ: "gptj",
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
MODEL_ARCH.STARCODER: "starcoder",
MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.NOMIC_BERT: "nomic-bert",
MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
MODEL_ARCH.QWEN2: "qwen2",
MODEL_ARCH.QWEN2MOE: "qwen2moe",
MODEL_ARCH.QWEN2VL: "qwen2vl",
MODEL_ARCH.PHI2: "phi2",
MODEL_ARCH.PHI3: "phi3",
MODEL_ARCH.PLAMO: "plamo",
MODEL_ARCH.CODESHELL: "codeshell",
MODEL_ARCH.ORION: "orion",
MODEL_ARCH.INTERNLM2: "internlm2",
MODEL_ARCH.MINICPM: "minicpm",
MODEL_ARCH.MINICPM3: "minicpm3",
MODEL_ARCH.GEMMA: "gemma",
MODEL_ARCH.GEMMA2: "gemma2",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.RWKV6: "rwkv6",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.OLMO2: "olmo2",
MODEL_ARCH.OLMOE: "olmoe",
MODEL_ARCH.OPENELM: "openelm",
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK: "deepseek",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.CHATGLM: "chatglm",
MODEL_ARCH.BITNET: "bitnet",
MODEL_ARCH.T5: "t5",
MODEL_ARCH.T5ENCODER: "t5encoder",
MODEL_ARCH.JAIS: "jais",
MODEL_ARCH.NEMOTRON: "nemotron",
MODEL_ARCH.EXAONE: "exaone",
MODEL_ARCH.GRANITE: "granite",
MODEL_ARCH.GRANITE_MOE: "granitemoe",
MODEL_ARCH.CHAMELEON: "chameleon",
MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -534,6 +563,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
MODEL_TENSOR.CLS: "cls",
MODEL_TENSOR.CLS_OUT: "cls.output",
MODEL_TENSOR.CONV1D: "conv1d",
MODEL_TENSOR.CONVNEXT_DW: "convnext.{bid}.dw",
MODEL_TENSOR.CONVNEXT_NORM: "convnext.{bid}.norm",
MODEL_TENSOR.CONVNEXT_PW1: "convnext.{bid}.pw1",
MODEL_TENSOR.CONVNEXT_PW2: "convnext.{bid}.pw2",
MODEL_TENSOR.CONVNEXT_GAMMA: "convnext.{bid}.gamma",
MODEL_TENSOR.POSNET_CONV1: "posnet.{bid}.conv1",
MODEL_TENSOR.POSNET_CONV2: "posnet.{bid}.conv2",
MODEL_TENSOR.POSNET_NORM: "posnet.{bid}.norm",
MODEL_TENSOR.POSNET_NORM1: "posnet.{bid}.norm1",
MODEL_TENSOR.POSNET_NORM2: "posnet.{bid}.norm2",
MODEL_TENSOR.POSNET_ATTN_NORM: "posnet.{bid}.attn_norm",
MODEL_TENSOR.POSNET_ATTN_Q: "posnet.{bid}.attn_q",
MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k",
MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v",
MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -1372,6 +1417,28 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.WAVTOKENIZER_DEC: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
MODEL_TENSOR.CONV1D,
MODEL_TENSOR.CONVNEXT_DW,
MODEL_TENSOR.CONVNEXT_NORM,
MODEL_TENSOR.CONVNEXT_PW1,
MODEL_TENSOR.CONVNEXT_PW2,
MODEL_TENSOR.CONVNEXT_GAMMA,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.POSNET_CONV1,
MODEL_TENSOR.POSNET_CONV2,
MODEL_TENSOR.POSNET_NORM,
MODEL_TENSOR.POSNET_NORM1,
MODEL_TENSOR.POSNET_NORM2,
MODEL_TENSOR.POSNET_ATTN_NORM,
MODEL_TENSOR.POSNET_ATTN_Q,
MODEL_TENSOR.POSNET_ATTN_K,
MODEL_TENSOR.POSNET_ATTN_V,
MODEL_TENSOR.POSNET_ATTN_OUT,
],
# TODO
}

21
gguf-py/gguf/gguf_writer.py
View file

@ -631,6 +631,21 @@ class GGUFWriter:
def add_embedding_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_features_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.FEATURES_LENGTH.format(arch=self.arch), length)
def add_posnet_embedding_length(self, length: int) -> None:
self.add_uint32(Keys.PosNet.EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_posnet_block_count(self, length: int) -> None:
self.add_uint32(Keys.PosNet.BLOCK_COUNT.format(arch=self.arch), length)
def add_convnext_embedding_length(self, length: int) -> None:
self.add_uint32(Keys.ConvNext.EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_convnext_block_count(self, length: int) -> None:
self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length)
def add_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
@ -727,6 +742,12 @@ class GGUFWriter:
def add_layer_norm_rms_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
def add_group_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.GROUPNORM_EPS.format(arch=self.arch), value)
def add_group_norm_groups(self, value: int) -> None:
self.add_uint32(Keys.Attention.GROUPNORM_GROUPS.format(arch=self.arch), value)
def add_causal_attention(self, value: bool) -> None:
self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)

70
gguf-py/gguf/tensor_mapping.py
View file

@ -42,6 +42,7 @@ class TensorNameMap:
"emb_ln", # nomic-bert
"transformer.norm", # openelm
"rwkv.blocks.0.pre_ln", # rwkv
"backbone.norm", # wavtokenizer
),
# Position embeddings
@ -60,6 +61,7 @@ class TensorNameMap:
"lm_head.linear", # phi2
"output_layer", # chatglm
"head", # rwkv
"head.out", # wavtokenizer
),
# Output norm
@ -80,6 +82,7 @@ class TensorNameMap:
"transformer.norm", # openelm
"model.norm", # nemotron
"rwkv.ln_out", # rwkv
"backbone.final_layer_norm", # wavtokenizer
),
# Rope frequencies
@ -90,6 +93,10 @@ class TensorNameMap:
MODEL_TENSOR.ROPE_FACTORS_LONG: (),
MODEL_TENSOR.ROPE_FACTORS_SHORT: (),
MODEL_TENSOR.CONV1D: (
"backbone.embed", # roberta
),
}
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
@ -681,6 +688,8 @@ class TensorNameMap:
"encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
),
############################################################################
# TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
MODEL_TENSOR.ENC_OUTPUT_NORM: (
"encoder.final_layer_norm", # t5
),
@ -693,6 +702,67 @@ class TensorNameMap:
MODEL_TENSOR.CLS_OUT: (
"classifier.out_proj", # roberta
),
#############################################################################
MODEL_TENSOR.CONVNEXT_DW: (
"backbone.convnext.{bid}.dwconv", # wavtokenizer
),
MODEL_TENSOR.CONVNEXT_NORM: (
"backbone.convnext.{bid}.norm", # wavtokenizer
),
MODEL_TENSOR.CONVNEXT_PW1: (
"backbone.convnext.{bid}.pwconv1", # wavtokenizer
),
MODEL_TENSOR.CONVNEXT_PW2: (
"backbone.convnext.{bid}.pwconv2", # wavtokenizer
),
MODEL_TENSOR.CONVNEXT_GAMMA: (
"backbone.convnext.{bid}.gamma", # wavtokenizer
),
MODEL_TENSOR.POSNET_CONV1: (
"backbone.posnet.{bid}.conv1", # wavtokenizer
),
MODEL_TENSOR.POSNET_CONV2: (
"backbone.posnet.{bid}.conv2", # wavtokenizer
),
MODEL_TENSOR.POSNET_NORM: (
"backbone.posnet.{bid}.norm", # wavtokenizer
),
MODEL_TENSOR.POSNET_NORM1: (
"backbone.posnet.{bid}.norm1", # wavtokenizer
),
MODEL_TENSOR.POSNET_NORM2: (
"backbone.posnet.{bid}.norm2", # wavtokenizer
),
MODEL_TENSOR.POSNET_ATTN_NORM: (
"backbone.posnet.{bid}.norm", # wavtokenizer
),
MODEL_TENSOR.POSNET_ATTN_Q: (
"backbone.posnet.{bid}.q", # wavtokenizer
),
MODEL_TENSOR.POSNET_ATTN_K: (
"backbone.posnet.{bid}.k", # wavtokenizer
),
MODEL_TENSOR.POSNET_ATTN_V: (
"backbone.posnet.{bid}.v", # wavtokenizer
),
MODEL_TENSOR.POSNET_ATTN_OUT: (
"backbone.posnet.{bid}.proj_out", # wavtokenizer
),
}
# architecture-specific block mappings

2
gguf-py/tests/test_quants.py
View file

@ -136,7 +136,7 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
sum_diff_bits = np.sum(diff_bits)
logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)")
logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)")
return False

17
include/llama.h
View file

@ -482,9 +482,6 @@ extern "C" {
// Returns the total number of parameters in the model
LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
// Get a llama model tensor
LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
// Returns true if the model contains an encoder that requires llama_encode() call
LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
@ -1139,16 +1136,12 @@ extern "C" {
const char * grammar_str,
const char * grammar_root);
/// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
int32_t n_vocab, // llama_n_vocab()
llama_token special_eos_id, // llama_token_eos()
llama_token linefeed_id, // llama_token_nl()
int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat, // 1.0 = disabled
float penalty_freq, // 0.0 = disabled
float penalty_present, // 0.0 = disabled
bool penalize_nl, // consider newlines as a repeatable token
bool ignore_eos); // ignore the end-of-sequence token
int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat, // 1.0 = disabled
float penalty_freq, // 0.0 = disabled
float penalty_present); // 0.0 = disabled
/// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
LLAMA_API struct llama_sampler * llama_sampler_init_dry(

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
74d66b63eaf207a24f3e93bb922aba131cbf2906
e6d93f40dffe8733d5d72f1d8fa6b3ca27ae899f

125
src/llama-sampling.cpp
View file

@ -1396,19 +1396,15 @@ struct llama_sampler * llama_sampler_init_grammar_impl(const struct llama_vocab
// penalties
struct llama_sampler_penalties {
const int32_t n_vocab;
const llama_token special_eos_id;
const llama_token linefeed_id;
const int32_t penalty_last_n;
const float penalty_repeat;
const float penalty_freq;
const float penalty_present;
const bool penalize_nl;
const bool ignore_eos;
ring_buffer<llama_token> prev;
// a frequency map to count token occurrences
std::unordered_map<llama_token, int> token_count;
};
static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) {
@ -1421,76 +1417,50 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to
return;
}
ctx->token_count[token]++;
// if the ring buffer is full, remove the oldest token
if (ctx->prev.size() >= (size_t) ctx->penalty_last_n) {
const auto old = ctx->prev.front();
ctx->token_count[old]--;
if (ctx->token_count[old] == 0) {
ctx->token_count.erase(old);
}
}
ctx->prev.push_back(token);
#if 0
// sanity check
std::unordered_map<llama_token, int> tmp;
for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
tmp[ctx->prev.rat(i)]++;
}
assert(ctx->token_count == tmp);
#endif
}
static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
if (ctx->ignore_eos) {
assert(ctx->special_eos_id >= 0);
// optimistically check if the candidates are not yet sorted/shuffled/truncated
if (cur_p->size > (size_t) ctx->special_eos_id && cur_p->data[ctx->special_eos_id].id == ctx->special_eos_id) {
cur_p->data[ctx->special_eos_id].logit = -INFINITY;
} else {
// else, search for the special EOS token
for (size_t i = 0; i < cur_p->size; ++i) {
if (cur_p->data[i].id == ctx->special_eos_id) {
cur_p->data[i].logit = -INFINITY;
break;
}
}
}
}
if ((ctx->penalty_last_n == 0) ||
(ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) {
return;
}
bool nl_found = false;
size_t nl_idx = 0;
float nl_logit = -INFINITY;
if (!ctx->penalize_nl) {
assert(ctx->linefeed_id >= 0);
// optimistically check if the candidates are not yet sorted/shuffled/truncated
if (cur_p->size > (size_t) ctx->linefeed_id && cur_p->data[ctx->linefeed_id].id == ctx->linefeed_id) {
nl_found = true;
nl_idx = ctx->linefeed_id;
nl_logit = cur_p->data[ctx->linefeed_id].logit;
} else {
// else, search for the linefeed token
for (size_t i = 0; i < cur_p->size; ++i) {
if (cur_p->data[i].id == ctx->linefeed_id) {
nl_found = true;
nl_idx = i;
nl_logit = cur_p->data[i].logit;
break;
}
}
}
}
// Create a frequency map to count occurrences of each token in last_tokens
// TODO: optimize this by maintaining the token count in the sampler context
using llama_token_cnt = std::unordered_map<llama_token, int>;
llama_token_cnt token_count;
for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
token_count[ctx->prev.rat(i)]++;
}
// Apply frequency and presence penalties to the cur_p
for (size_t i = 0; i < cur_p->size; ++i) {
const auto token_iter = token_count.find(cur_p->data[i].id);
if (token_iter == token_count.end()) {
const auto token_iter = ctx->token_count.find(cur_p->data[i].id);
if (token_iter == ctx->token_count.end()) {
continue;
}
const int count = token_iter->second;
assert(count > 0 && count <= ctx->penalty_last_n);
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
if (cur_p->data[i].logit <= 0) {
@ -1503,30 +1473,21 @@ static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_tok
}
cur_p->sorted = false;
if (!ctx->penalize_nl && nl_found) {
// restore the logit of the newline token if it was penalized
cur_p->data[nl_idx].logit = nl_logit;
}
}
static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
ctx->prev.clear();
ctx->token_count.clear();
}
static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
auto * result = llama_sampler_init_penalties(
ctx->n_vocab,
ctx->special_eos_id,
ctx->linefeed_id,
ctx->penalty_last_n,
ctx->penalty_repeat,
ctx->penalty_freq,
ctx->penalty_present,
ctx->penalize_nl,
ctx->ignore_eos);
ctx->penalty_present);
// copy the state
{
@ -1552,38 +1513,21 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
};
struct llama_sampler * llama_sampler_init_penalties(
int32_t n_vocab,
llama_token special_eos_id,
llama_token linefeed_id,
int32_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present,
bool penalize_nl,
bool ignore_eos) {
if (linefeed_id == LLAMA_TOKEN_NULL) {
penalize_nl = true;
}
if (special_eos_id == LLAMA_TOKEN_NULL) {
ignore_eos = false;
}
float penalty_present) {
penalty_last_n = std::max(penalty_last_n, 0);
return new llama_sampler {
/* .iface = */ &llama_sampler_penalties_i,
/* .ctx = */ new llama_sampler_penalties {
/* .n_vocab = */ n_vocab,
/* .special_eos_id = */ special_eos_id,
/* .linefeed_id = */ linefeed_id,
/* .penalty_last_n = */ penalty_last_n,
/* .penalty_repeat = */ penalty_repeat,
/* .penalty_freq = */ penalty_freq,
/* .penalty_present = */ penalty_present,
/* .penalize_nl = */ penalize_nl,
/* .ignore_eos = */ ignore_eos,
/* .prev = */ ring_buffer<llama_token>(penalty_last_n),
/* .token_count = */ {},
},
};
}
@ -1611,7 +1555,8 @@ static void get_overlapping_token_sequences(const llama_vocab & vocab, const std
if (word.find(str) != std::string::npos) {
token_sequences.emplace(token_id, std::vector<llama_token>());
} else {
size_t word_len = word.size(), str_len = str.size();
size_t word_len = word.size();
size_t str_len = str.size();
size_t pos = -1;
while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
bool match = true;

6
src/llama-vocab.cpp
View file

@ -738,7 +738,7 @@ struct llm_tokenizer_wpm_session {
std::vector<std::string> words(1, "");
for (const uint32_t cpt : cpts_nfd) {
const auto flags = unicode_cpt_flags(cpt);
const auto flags = unicode_cpt_flags_from_cpt(cpt);
if (flags.is_whitespace) {
if (words.back().size()) { // finish previous word if any
@ -1867,6 +1867,10 @@ int32_t llama_detokenize_impl(
int32_t text_len_max,
bool remove_special,
bool unparse_special) {
if (vocab.type == LLAMA_VOCAB_TYPE_NONE) {
return 0;
}
GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first.");
int32_t avail = text_len_max;

977
src/llama.cpp
View file

File diff suppressed because it is too large Load diff

102
src/unicode.cpp
View file

@ -71,15 +71,15 @@ uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
throw std::invalid_argument("failed to convert utf8 to codepoint");
}
//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cp) {
//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cpt) {
// std::vector<uint16_t> result;
// if (/* 0x0000 <= cp && */ cp <= 0xffff) {
// result.emplace_back(cp);
// if (/* 0x0000 <= cpt && */ cpt <= 0xffff) {
// result.emplace_back(cpt);
// return result;
// }
// if (0x10000 <= cp && cp <= 0x10ffff) {
// result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
// result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
// if (0x10000 <= cpt && cpt <= 0x10ffff) {
// result.emplace_back(0xd800 | ((cpt - 0x10000) >> 10));
// result.emplace_back(0xdc00 | ((cpt - 0x10000) & 0x03ff));
// return result;
// }
// throw std::invalid_argument("failed to convert codepoint to utf16");
@ -120,8 +120,8 @@ uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
// return result;
//}
static std::vector<codepoint_flags> unicode_cpt_flags_array() {
std::vector<codepoint_flags> cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED);
static std::vector<unicode_cpt_flags> unicode_cpt_flags_array() {
std::vector<unicode_cpt_flags> cpt_flags(MAX_CODEPOINTS, unicode_cpt_flags::UNDEFINED);
assert (unicode_ranges_flags.begin()[0].first == 0);
assert (unicode_ranges_flags.begin()[unicode_ranges_flags.size()-1].first == MAX_CODEPOINTS);
@ -253,8 +253,8 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
};
auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
};
size_t _prev_end = offset_ini;
@ -371,8 +371,8 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
};
auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
};
size_t _prev_end = offset_ini;
@ -572,29 +572,29 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
// interface
//
std::string unicode_cpt_to_utf8(uint32_t cp) {
std::string unicode_cpt_to_utf8(uint32_t cpt) {
std::string result;
if (/* 0x00 <= cp && */ cp <= 0x7f) {
result.push_back(cp);
if (/* 0x00 <= cpt && */ cpt <= 0x7f) {
result.push_back(cpt);
return result;
}
if (0x80 <= cp && cp <= 0x7ff) {
result.push_back(0xc0 | ((cp >> 6) & 0x1f));
result.push_back(0x80 | (cp & 0x3f));
if (0x80 <= cpt && cpt <= 0x7ff) {
result.push_back(0xc0 | ((cpt >> 6) & 0x1f));
result.push_back(0x80 | (cpt & 0x3f));
return result;
}
if (0x800 <= cp && cp <= 0xffff) {
result.push_back(0xe0 | ((cp >> 12) & 0x0f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
if (0x800 <= cpt && cpt <= 0xffff) {
result.push_back(0xe0 | ((cpt >> 12) & 0x0f));
result.push_back(0x80 | ((cpt >> 6) & 0x3f));
result.push_back(0x80 | (cpt & 0x3f));
return result;
}
if (0x10000 <= cp && cp <= 0x10ffff) {
result.push_back(0xf0 | ((cp >> 18) & 0x07));
result.push_back(0x80 | ((cp >> 12) & 0x3f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
if (0x10000 <= cpt && cpt <= 0x10ffff) {
result.push_back(0xf0 | ((cpt >> 18) & 0x07));
result.push_back(0x80 | ((cpt >> 12) & 0x3f));
result.push_back(0x80 | ((cpt >> 6) & 0x3f));
result.push_back(0x80 | (cpt & 0x3f));
return result;
}
@ -624,19 +624,19 @@ std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
return result;
}
codepoint_flags unicode_cpt_flags(const uint32_t cp) {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
unicode_cpt_flags unicode_cpt_flags_from_cpt(const uint32_t cpt) {
static const unicode_cpt_flags undef(unicode_cpt_flags::UNDEFINED);
static const auto cpt_flags = unicode_cpt_flags_array();
return cp < cpt_flags.size() ? cpt_flags[cp] : undef;
return cpt < cpt_flags.size() ? cpt_flags[cpt] : undef;
}
codepoint_flags unicode_cpt_flags(const std::string & utf8) {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
unicode_cpt_flags unicode_cpt_flags_from_utf8(const std::string & utf8) {
static const unicode_cpt_flags undef(unicode_cpt_flags::UNDEFINED);
if (utf8.empty()) {
return undef; // undefined
}
size_t offset = 0;
return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset));
return unicode_cpt_flags_from_cpt(unicode_cpt_from_utf8(utf8, offset));
}
std::string unicode_byte_to_utf8(uint8_t byte) {
@ -649,41 +649,41 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) {
return map.at(utf8);
}
uint32_t unicode_tolower(uint32_t cp) {
uint32_t unicode_tolower(uint32_t cpt) {
// binary search
auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cp,
auto it = std::lower_bound(unicode_map_lowercase.begin(), unicode_map_lowercase.end(), cpt,
[](const std::pair<uint32_t, uint32_t> & pair, uint32_t value) {
return pair.first < value;
});
if (it != unicode_map_lowercase.end() && it->first == cp) {
if (it != unicode_map_lowercase.end() && it->first == cpt) {
return it->second;
}
return cp; // Return the original code point if no lowercase mapping is found
return cpt; // Return the original code point if no lowercase mapping is found
}
std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
// unicode categories
static const std::map<std::string, int> k_ucat_enum = {
{ "\\p{N}", codepoint_flags::NUMBER },
{ "\\p{L}", codepoint_flags::LETTER },
{ "\\p{P}", codepoint_flags::PUNCTUATION },
{ "\\p{N}", unicode_cpt_flags::NUMBER },
{ "\\p{L}", unicode_cpt_flags::LETTER },
{ "\\p{P}", unicode_cpt_flags::PUNCTUATION },
};
static const std::map<int, int> k_ucat_cpt = {
{ codepoint_flags::NUMBER, 0xD1 },
{ codepoint_flags::LETTER, 0xD2 },
{ codepoint_flags::PUNCTUATION, 0xD3 },
{ unicode_cpt_flags::NUMBER, 0xD1 },
{ unicode_cpt_flags::LETTER, 0xD2 },
{ unicode_cpt_flags::PUNCTUATION, 0xD3 },
};
static const std::map<int, std::string> k_ucat_map = {
{ codepoint_flags::NUMBER, "\x30-\x39" }, // 0-9
{ codepoint_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z
{ codepoint_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
{ unicode_cpt_flags::NUMBER, "\x30-\x39" }, // 0-9
{ unicode_cpt_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z
{ unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
};
// compute collapsed codepoints only if needed by at least one regex
bool need_collapse = false;
for (auto & regex_expr : regex_exprs) {
for (const auto & regex_expr : regex_exprs) {
// search for unicode categories
for (const auto & ucat : k_ucat_enum) {
if (std::string::npos != regex_expr.find(ucat.first)) {
@ -709,7 +709,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
continue;
}
const auto flags = unicode_cpt_flags(cpts[i]);
const auto flags = unicode_cpt_flags_from_cpt(cpts[i]);
if (flags.is_whitespace) {
//NOTE: C++ std::regex \s does not mach 0x85, Rust and Python regex does.
@ -725,7 +725,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
std::vector<size_t> bpe_offsets = { cpts.size() };
for (auto & regex_expr : regex_exprs) {
for (const auto & regex_expr : regex_exprs) {
// first, see if we have an efficient custom regex implementation
auto tmp = unicode_regex_split_custom(text, regex_expr, bpe_offsets);
@ -739,7 +739,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
// if a unicode category is used in the regex, we use the collapsed text and replace the unicode category
// with the corresponding collapsed representation
bool use_collapsed = false;
for (auto & ucat : k_ucat_enum) {
for (const auto & ucat : k_ucat_enum) {
if (std::string::npos != regex_expr.find(ucat.first)) {
use_collapsed = true;
break;
@ -805,7 +805,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
// std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
std::wstring wtext(cpts.begin(), cpts.end());
for (size_t i = 0; i < wtext.size(); ++i) {
if (wtext[i] > 0x7F && unicode_cpt_flags(wtext[i]).is_whitespace) {
if (wtext[i] > 0x7F && unicode_cpt_flags_from_cpt(wtext[i]).is_whitespace) {
wtext[i] = 0x0B;
}
}

19
src/unicode.h
View file

@ -4,9 +4,7 @@
#include <string>
#include <vector>
// TODO: prefix all symbols with "llama_"
struct codepoint_flags {
struct unicode_cpt_flags {
enum {
UNDEFINED = 0x0001,
NUMBER = 0x0002, // regex: \p{N}
@ -35,7 +33,7 @@ struct codepoint_flags {
uint16_t is_nfd : 1;
// decode from uint16
inline codepoint_flags(const uint16_t flags=0) {
inline unicode_cpt_flags(const uint16_t flags = 0) {
*reinterpret_cast<uint16_t*>(this) = flags;
}
@ -50,18 +48,19 @@ struct codepoint_flags {
size_t unicode_len_utf8(char src);
std::string unicode_cpt_to_utf8(uint32_t cp);
uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
std::string unicode_cpt_to_utf8 (uint32_t cpt);
uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
codepoint_flags unicode_cpt_flags(const uint32_t cp);
codepoint_flags unicode_cpt_flags(const std::string & utf8);
unicode_cpt_flags unicode_cpt_flags_from_cpt (uint32_t cpt);
unicode_cpt_flags unicode_cpt_flags_from_utf8(const std::string & utf8);
std::string unicode_byte_to_utf8(uint8_t byte);
uint8_t unicode_utf8_to_byte(const std::string & utf8);
uint8_t unicode_utf8_to_byte(const std::string & utf8);
uint32_t unicode_tolower(uint32_t cp);
uint32_t unicode_tolower(uint32_t cpt);
std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);

1
tests/CMakeLists.txt
View file

@ -129,6 +129,7 @@ llama_target_and_test(test-arg-parser.cpp)
llama_target_and_test(test-chat-template.cpp)
# llama_target_and_test(test-opt.cpp) # SLOW
llama_target_and_test(test-gguf.cpp)
llama_target_and_test(test-backend-ops.cpp)
llama_target_and_test(test-model-load-cancel.cpp LABEL "model")

4
tests/test-backend-ops.cpp
View file

@ -3549,8 +3549,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
for (ggml_type type_dst : all_types) {
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
}
}
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {

1303
tests/test-gguf.cpp Normal file
View file

File diff suppressed because it is too large Load diff

2
tests/test-sampling.cpp
View file

@ -145,7 +145,7 @@ static void test_penalties(
sampler_tester tester(probs, probs_expected);
const size_t n_vocab = probs.size();
auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false);
auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);