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Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	CMakeLists.txt
#	Makefile
#	README.md
#	flake.lock
#	llama.cpp
This commit is contained in:
Concedo 2024-02-07 22:21:32 +08:00
commit ec2dbd99a3
21 changed files with 2614 additions and 1863 deletions
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10
README-sycl.md
View file

@ -311,15 +311,13 @@ Output (example):
a. Download & install cmake for Windows: https://cmake.org/download/
b. Download & install make for Windows provided by mingw-w64
b. Download & install mingw-w64 make for Windows provided by w64devkit
- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases.
- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z).
- Extract `w64devkit` on your pc.
- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**.
- Add the **bin** folder path in the Windows system PATH environment.
- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`.
### Build locally:

22
common/common.cpp
View file

@ -47,6 +47,10 @@
#define GGML_USE_CUBLAS_SYCL
#endif
#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUBLAS_SYCL_VULKAN
#endif
int32_t get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
@ -400,6 +404,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
sparams.penalty_present = std::stof(argv[i]);
} else if (arg == "--dynatemp-range") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.dynatemp_range = std::stof(argv[i]);
} else if (arg == "--dynatemp-exp") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.dynatemp_exponent = std::stof(argv[i]);
} else if (arg == "--mirostat") {
if (++i >= argc) {
invalid_param = true;
@ -649,8 +665,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
params.tensor_split[i] = 0.0f;
}
}
#ifndef GGML_USE_CUBLAS_SYCL
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n");
#ifndef GGML_USE_CUBLAS_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n");
#endif // GGML_USE_CUBLAS_SYCL
} else if (arg == "--no-mmap") {
params.use_mmap = false;
@ -943,6 +959,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range);
printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent);
printf(" --mirostat N use Mirostat sampling.\n");
printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);

78
convert-hf-to-gguf.py
View file

@ -22,6 +22,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from convert import HfVocab
# check for any of the given keys in the dictionary and return the value of the first key found
def get_key_opts(d, keys):
@ -205,6 +207,8 @@ class Model:
return OrionModel
if model_architecture == "InternLM2ForCausalLM":
return InternLM2Model
if model_architecture == "MiniCPMForCausalLM":
return MiniCPMModel
return Model
def _is_model_safetensors(self) -> bool:
@ -258,6 +262,8 @@ class Model:
return gguf.MODEL_ARCH.ORION
if arch == "InternLM2ForCausalLM":
return gguf.MODEL_ARCH.INTERNLM2
if arch == "MiniCPMForCausalLM":
return gguf.MODEL_ARCH.MINICPM
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -402,6 +408,31 @@ class Model:
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_hf(self):
path = self.dir_model
added_tokens_path = self.dir_model
vocab = HfVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
tokens = []
scores = []
toktypes = []
for text, score, toktype in vocab.all_tokens():
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
assert len(tokens) == vocab.vocab_size
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
class GPTNeoXModel(Model):
def set_gguf_parameters(self):
@ -1041,6 +1072,24 @@ class MixtralModel(Model):
self._set_vocab_sentencepiece()
class MiniCPMModel(Model):
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
self.gguf_writer.add_name("MiniCPM")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
def set_vocab(self):
self._set_vocab_hf()
class QwenModel(Model):
@staticmethod
def token_bytes_to_string(b):
@ -1416,8 +1465,32 @@ class InternLM2Model(Model):
self.gguf_writer.add_add_space_prefix(add_prefix)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
old_eos = special_vocab.special_token_ids["eos"]
if "chat" in os.path.basename(self.dir_model.absolute()):
# For the chat model, we replace the eos with '<|im_end|>'.
special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
in chat mode so that the conversation can end normally.")
special_vocab.add_to_gguf(self.gguf_writer)
def _try_get_sft_eos(self, tokenizer):
unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
im_end_list = tokenizer.encode('<|im_end|>')
assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
if len(unused_145_list) == 1:
eos_token = unused_145_list[0]
if len(im_end_list) == 1:
eos_token = im_end_list[0]
return eos_token
def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def set_gguf_parameters(self):
self.gguf_writer.add_name("InternLM2")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
@ -1486,8 +1559,9 @@ class InternLM2Model(Model):
qkv = data_torch
qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
q = rearrange(q, " o g n i -> o (g n i)").T
k = rearrange(k, " o g n i -> o (g n i)").T
# The model weights of q and k equire additional reshape.
q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads)
k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads)
v = rearrange(v, " o g n i -> o (g n i)").T
self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)

14
convert.py
View file

@ -334,9 +334,9 @@ class Params:
class BpeVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
try:
if isinstance(self.bpe_tokenizer.get('model'), dict):
self.vocab = self.bpe_tokenizer["model"]["vocab"]
except KeyError:
else:
self.vocab = self.bpe_tokenizer
added_tokens: dict[str, int]
if fname_added_tokens is not None:
@ -515,10 +515,14 @@ class HfVocab:
# Yield token text, score, and type
yield token_text, self.get_token_score(token_id), self.get_token_type(
token_id, self.special_ids # Reuse already stored special IDs
token_id, token_text, self.special_ids # Reuse already stored special IDs
)
def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType:
def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
# Special case for byte tokens
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
return gguf.TokenType.BYTE
# Determine token type based on whether it's a special token
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
@ -530,7 +534,7 @@ class HfVocab:
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
if text in self.specials:
toktype = self.get_token_type(self.specials[text], self.special_ids)
toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED

14
examples/llava/llava-cli.cpp
View file

@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
return true;
}
@ -152,20 +152,8 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
size_t image_pos = prompt.find("<image>");
if (image_pos != std::string::npos) {
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
system_prompt = prompt.substr(0, image_pos);
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
// We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string
size_t pos = 0;
while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) {
user_prompt.replace(pos, 2, "\n");
pos += 1; // Advance past the replaced newline
}
while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) {
system_prompt.replace(pos, 2, "\n");
pos += 1; // Advance past the replaced newline
}
printf("system_prompt: %s\n", system_prompt.c_str());
printf("user_prompt: %s\n", user_prompt.c_str());
} else {

22
examples/server/README.md
View file

@ -137,6 +137,10 @@ node index.js
`temperature`: Adjust the randomness of the generated text (default: 0.8).
`dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled).
`dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
@ -264,7 +268,23 @@ Notice that each `probs` is an array of length `n_probs`.
It also accepts all the options of `/completion` except `stream` and `prompt`.
- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- **GET** `/props`: Return current server settings.
### Result JSON
```json
{
"assistant_name": "",
"user_name": "",
"default_generation_settings": { ... },
"total_slots": 1
}
```
- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.

448
examples/server/completion.js.hpp
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@ -236,214 +236,250 @@ unsigned char completion_js[] = {
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};
unsigned int completion_js_len = 5346;
unsigned int completion_js_len = 5782;

3
examples/server/public/completion.js
View file

@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => {
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
if (!generation_settings) {
generation_settings = await fetch("/model.json").then(r => r.json());
const props = await fetch("/props").then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;
}

99
examples/server/server.cpp
View file

@ -335,6 +335,7 @@ struct llama_server_context
// slots / clients
std::vector<llama_client_slot> slots;
json default_generation_settings_for_props;
llama_server_queue queue_tasks;
llama_server_response queue_results;
@ -431,6 +432,9 @@ struct llama_server_context
slots.push_back(slot);
}
default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["seed"] = -1;
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
// empty system prompt
@ -521,27 +525,29 @@ struct llama_server_context
slot->oaicompat_model = "";
}
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
slot->params.seed = json_value(data, "seed", default_params.seed);
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
slot->params.seed = json_value(data, "seed", default_params.seed);
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
// infill
if (data.count("input_prefix") != 0)
@ -984,11 +990,6 @@ struct llama_server_context
queue_results.send(res);
}
json get_model_props()
{
return get_formated_generation(slots[0]);
}
json get_formated_generation(llama_client_slot &slot)
{
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@ -999,6 +1000,8 @@ struct llama_server_context
{"model", params.model_alias},
{"seed", slot.params.seed},
{"temperature", slot.sparams.temp},
{"dynatemp_range", slot.sparams.dynatemp_range},
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
@ -1160,13 +1163,30 @@ struct llama_server_context
task.multitask_id = multitask_id;
// when a completion task's prompt array is not a singleton, we split it into multiple requests
if (task.data.count("prompt") && task.data.at("prompt").size() > 1)
{
split_multiprompt_task(task_id, task);
}
// otherwise, it's a single-prompt task, we actually queue it
queue_tasks.post(task);
// if there's numbers in the prompt array it will be treated as an array of tokens
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
bool numbers = false;
for (const auto& e : task.data.at("prompt")) {
if (e.is_number()) {
numbers = true;
break;
}
}
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
// it will completely stall the server. I don't know where the bug for this is.
//
// if there are numbers, it needs to be treated like a single prompt,
// queue_tasks handles a mix of strings and numbers just fine.
if (numbers) {
queue_tasks.post(task);
} else {
split_multiprompt_task(task_id, task);
}
} else {
queue_tasks.post(task);
}
}
// for multiple images processing
@ -1248,7 +1268,10 @@ struct llama_server_context
void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
{
int prompt_count = multiprompt_task.data.at("prompt").size();
assert(prompt_count > 1);
if (prompt_count <= 1) {
send_error(multiprompt_task, "error while handling multiple prompts");
return;
}
// generate all the ID for subtask
std::vector<int> subtask_ids(prompt_count);
@ -2615,7 +2638,9 @@ int main(int argc, char **argv)
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
{ "user_name", llama.name_user.c_str() },
{ "assistant_name", llama.name_assistant.c_str() }
{ "assistant_name", llama.name_assistant.c_str() },
{ "default_generation_settings", llama.default_generation_settings_for_props },
{ "total_slots", llama.params.n_parallel }
};
res.set_content(data.dump(), "application/json; charset=utf-8");
});
@ -2866,12 +2891,6 @@ int main(int argc, char **argv)
}
});
svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
{
const json data = llama.get_model_props();
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
{ return res.set_content("", "application/json; charset=utf-8"); });

2
expose.cpp
View file

@ -60,7 +60,7 @@ extern "C"
putenv((char*)deviceenv.c_str());
int vulkan_info = inputs.vulkan_info;
vulkandeviceenv = "GGML_VULKAN_DEVICE="+std::to_string(vulkan_info);
vulkandeviceenv = "GGML_VK_VISIBLE_DEVICES="+std::to_string(vulkan_info);
putenv((char*)vulkandeviceenv.c_str());
executable_path = inputs.executable_path;

254
ggml-cuda.cu
View file

@ -5311,41 +5311,50 @@ template <bool need_check> static __global__ void
#endif // __CUDA_ARCH__ >= CC_VOLTA
}
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
template <int ncols_y_template, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) {
const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par;
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row >= nrows) {
if (row >= nrows_x) {
return;
}
const int blocks_per_row = ncols / qk;
const int blocks_per_row_x = ncols_x / qk;
const int blocks_per_col_y = nrows_y / QK8_1;
const int blocks_per_warp = vdr * WARP_SIZE / qi;
// partial sum for each thread
float tmp = 0.0f;
float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f};
const block_q_t * x = (const block_q_t *) vx;
const block_q8_1 * y = (const block_q8_1 *) vy;
for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) {
const int ibx = row*blocks_per_row + i; // x block index
for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) {
const int ibx = row*blocks_per_row_x + i; // x block index
const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
#pragma unroll
for (int j = 0; j < ncols_y; ++j) {
tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs);
}
}
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
for (int j = 0; j < ncols_y; ++j) {
tmp[j] = warp_reduce_sum(tmp[j]);
if (threadIdx.x == 0) {
dst[row] = tmp;
if (threadIdx.x == 0) {
dst[j*nrows_dst + row] = tmp[j];
}
}
}
@ -6817,121 +6826,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK4_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
static void mul_mat_vec_q_cuda(
const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK4_1 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
GGML_ASSERT(ncols_x % qk == 0);
GGML_ASSERT(ncols_y <= 4);
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK5_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK5_1 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK8_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
switch (ncols_y) {
case 1:
mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
break;
case 2:
mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
break;
case 3:
mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
break;
case 4:
mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
break;
// case 5:
// mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
// break;
// case 6:
// mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
// break;
// case 7:
// mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
// break;
// case 8:
// mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
// break;
default:
GGML_ASSERT(false);
// mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
break;
}
}
static void ggml_mul_mat_q4_0_q8_1_cuda(
@ -8439,7 +8383,7 @@ static void ggml_cuda_op_mul_mat_q(
CUDA_CHECK(cudaGetDevice(&id));
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
// nrows_dst == nrows of the matrix that the kernel writes into
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
switch (src0->type) {
@ -8570,50 +8514,73 @@ static void ggml_cuda_op_mul_mat_vec_q(
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream) {
GGML_ASSERT(ggml_nrows(src1) == 1);
const int64_t ne00 = src0->ne[0];
const int64_t row_diff = row_high - row_low;
const int64_t ne10 = src1->ne[0];
GGML_ASSERT(ne10 % QK8_1 == 0);
const int64_t ne0 = dst->ne[0];
int id;
CUDA_CHECK(cudaGetDevice(&id));
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
switch (src0->type) {
case GGML_TYPE_Q4_0:
mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q4_1:
mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q5_0:
mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q5_1:
mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q8_0:
mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q3_K:
mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q5_K:
mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_Q6_K:
mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ2_XXS:
mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ2_XS:
mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
default:
GGML_ASSERT(false);
@ -9943,17 +9910,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
#ifdef GGML_CUDA_FORCE_DMMV
const bool use_mul_mat_vec_q = false;
#else
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
#endif // GGML_CUDA_FORCE_DMMV
if (use_mul_mat_vec_q) {
// NOTE: this kernel does not support ggml_nrows(src1) > 1
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
}
} else {
if (use_mul_mat_q) {
if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else if (use_mul_mat_q) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
} else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);

2
ggml-impl.h
View file

@ -19,6 +19,7 @@ extern "C" {
// fall back to the _Static_assert C11 keyword.
// if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976
#ifndef __cplusplus
#ifndef static_assert
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
#define static_assert(cond, msg) _Static_assert(cond, msg)
@ -26,6 +27,7 @@ extern "C" {
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#endif
#endif
// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))

133
ggml-quants.c
View file

@ -2383,19 +2383,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
uint8_t L[QK_K];
uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float weights[32];
float mins[QK_K/32];
float scales[QK_K/32];
float sw[QK_K/32];
float mins[QK_K/32];
float scales[QK_K/32];
for (int i = 0; i < nb; i++) {
float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K;
float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j;
@ -2403,25 +2404,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
} else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
}
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
//scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
}
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
float inv_min = max_min > 0 ? 63.f/max_min : 0.f;
float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]);
uint8_t lm = nearest_int(inv_min*mins[j]);
ls = MIN(63, ls);
lm = MIN(63, lm);
uint8_t ls = Ls[j];
uint8_t lm = Lm[j];
if (j < 4) {
y[i].scales[j] = ls;
y[i].scales[j+4] = lm;
@ -2431,8 +2424,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6);
}
}
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) {
@ -2690,20 +2683,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
const int nb = n_per_row / QK_K;
uint8_t L[QK_K];
float mins[QK_K/32];
float scales[QK_K/32];
float weights[32];
uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float mins[QK_K/32];
float scales[QK_K/32];
float sw[QK_K/32];
float weights[32];
for (int i = 0; i < nb; i++) {
float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K;
float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j;
@ -2711,22 +2705,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
} else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
}
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
}
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
float inv_min = max_min > 0 ? 63.f/max_min : 0.f;
float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]);
uint8_t lm = nearest_int(inv_min*mins[j]);
uint8_t ls = Ls[j];
uint8_t lm = Lm[j];
ls = MIN(63, ls);
lm = MIN(63, lm);
if (j < 4) {
@ -2738,8 +2729,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6);
}
}
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) {
@ -9050,8 +9041,6 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
int8_t L[32];
int8_t Laux[32];
float waux[32];
bool is_on_grid[4];
bool is_on_grid_aux[4];
uint8_t block_signs[4];
uint32_t q2[2*(QK_K/32)];
@ -9101,10 +9090,11 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
memset(L, 0, 32);
continue;
}
float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight);
float eff_max = scale*kMaxQ;
float best = 0;
float scale = max/(2*kMaxQ-1);
for (int is = -9; is <= 9; ++is) {
float id = (2*kMaxQ-1+is*0.1f)/max;
for (int is = -6; is <= 6; ++is) {
float id = (2*kMaxQ-1+is*0.1f)/eff_max;
float this_scale = 1/id;
for (int k = 0; k < 4; ++k) {
for (int i = 0; i < 8; ++i) {
@ -9114,9 +9104,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
uint16_t u = 0;
for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
int grid_index = kmap_q2xs[u];
is_on_grid_aux[k] = true;
if (grid_index < 0) {
is_on_grid_aux[k] = false;
const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
}
@ -9130,16 +9118,12 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
}
if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
scale = sumqx/sumq2; best = scale*sumqx;
for (int i = 0; i < 32; ++i) L[i] = Laux[i];
for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k];
memcpy(L, Laux, 32);
}
}
int n_not_ongrid = 0;
for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
if (n_not_ongrid > 0 && scale > 0) {
if (scale > 0) {
float id = 1/scale;
for (int k = 0; k < 4; ++k) {
if (is_on_grid[k]) continue;
uint16_t u = 0;
for (int i = 0; i < 8; ++i) {
int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
@ -9195,49 +9179,10 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
float d = max_scale/31;
y[ibl].d = GGML_FP32_TO_FP16(d);
float id = 1/d;
float sumqx = 0, sumq2 = 0;
for (int ib = 0; ib < QK_K/32; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib]-1));
l = MAX(0, MIN(15, l));
q2[2*ib+1] |= ((uint32_t)l << 28);
const float * xb = xbl + 32*ib;
const float * qw = quant_weights + QK_K*ibl + 32*ib;
for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib);
const float db = d * (1 + 2*l);
uint32_t u = 0;
for (int k = 0; k < 4; ++k) {
const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127);
const float * xk = xb + 8*k;
const float * wk = weight + 8*k;
const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]);
float best_mse = 0; int best_index = aux8[k];
for (int j = 0; j < 8; ++j) {
float diff = db * grid[j] * signs[j] - xk[j];
best_mse += wk[j] * diff * diff;
}
for (int idx = 0; idx < 256; ++idx) {
grid = (const uint8_t *)(kgrid_q2xs + idx);
float mse = 0;
for (int j = 0; j < 8; ++j) {
float diff = db * grid[j] * signs[j] - xk[j];
mse += wk[j] * diff * diff;
}
if (mse < best_mse) {
best_mse = mse; best_index = idx;
}
}
u |= (best_index << 8*k);
grid = (const uint8_t *)(kgrid_q2xs + best_index);
//grid = (const uint8_t *)(kgrid_q2xs + aux8[k]);
for (int j = 0; j < 8; ++j) {
float q = db * grid[j] * signs[j];
sumqx += wk[j] * q * xk[j];
sumq2 += wk[j] * q * q;
}
}
q2[2*ib] = u;
if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2);
}
memcpy(y[ibl].qs, q2, QK_K/4);
}

117
ggml-quants.h
View file

@ -191,70 +191,74 @@ typedef struct {
} block_iq3_xxs;
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
#ifdef __cplusplus
extern "C" {
#endif
// Quantization
void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k);
void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k);
void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k);
void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k);
void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k);
void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k);
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k);
void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k);
void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k);
void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k);
void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k);
void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
// Dequantization
void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k);
void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k);
void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
// Dot product
void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
//
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
@ -276,3 +280,8 @@ void iq2xs_init_impl(int grid_size);
void iq2xs_free_impl(int grid_size);
void iq3xs_init_impl(int grid_size);
void iq3xs_free_impl(int grid_size);
#ifdef __cplusplus
}
#endif

194
ggml-sycl.cpp
View file

@ -7693,6 +7693,13 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
*dsti = *xi;
}
static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
const sycl::half *xi = (const sycl::half *)cxi;
float *dsti = (float *)cdsti;
*dsti = *xi;
}
static void cpy_1_i16_i16(const char * cxi, char * cdsti) {
const int16_t *xi = (const int16_t *)cxi;
int16_t *dsti = (int16_t *)cdsti;
@ -7709,9 +7716,9 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
template <cpy_kernel_t cpy_1>
static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12,
const sycl::nd_item<3> &item_ct1) {
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
@ -7721,15 +7728,17 @@ static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
// determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
// then combine those indices with the corresponding byte offsets to get the total offsets
const int i02 = i / (ne00*ne01);
const int i01 = (i - i02*ne01*ne00) / ne00;
const int i00 = i - i02*ne01*ne00 - i01*ne00;
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
const int i03 = i/(ne00 * ne01 * ne02);
const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
const int i12 = i / (ne10*ne11);
const int i11 = (i - i12*ne10*ne11) / ne10;
const int i10 = i - i12*ne10*ne11 - i11*ne10;
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
const int i13 = i/(ne10 * ne11 * ne12);
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
cpy_1(cx + x_offset, cdst + dst_offset);
}
@ -7823,9 +7832,9 @@ static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
template <cpy_kernel_t cpy_blck, int qk>
static void cpy_f32_q(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12,
const sycl::nd_item<3> &item_ct1) {
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2)) *
qk;
@ -7834,15 +7843,17 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne,
return;
}
const int i02 = i / (ne00*ne01);
const int i01 = (i - i02*ne01*ne00) / ne00;
const int i00 = (i - i02*ne01*ne00 - i01*ne00);
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
const int i03 = i/(ne00 * ne01 * ne02);
const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
const int i12 = i / (ne10*ne11);
const int i11 = (i - i12*ne10*ne11) / ne10;
const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk;
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
const int i13 = i/(ne10 * ne11 * ne12);
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
cpy_blck(cx + x_offset, cdst + dst_offset);
}
@ -10599,10 +10610,12 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl(
static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@ -10615,8 +10628,8 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, nb00, nb01,
nb02, ne10, ne11, nb10, nb11, nb12,
cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
@ -10624,10 +10637,12 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@ -10640,8 +10655,8 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
nb02, ne10, ne11, nb10, nb11, nb12,
cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
@ -10649,10 +10664,12 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
GGML_ASSERT(ne % QK8_0 == 0);
@ -10661,17 +10678,20 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(
cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, item_ct1);
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
GGML_ASSERT(ne % QK4_0 == 0);
@ -10680,17 +10700,20 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(
cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, item_ct1);
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
GGML_ASSERT(ne % QK4_1 == 0);
@ -10699,17 +10722,20 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(
cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, item_ct1);
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@ -10722,8 +10748,8 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
nb02, ne10, ne11, nb10, nb11, nb12,
cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
@ -10731,10 +10757,12 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@ -10747,8 +10775,8 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
nb02, ne10, ne11, nb10, nb11, nb12,
cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
@ -10756,10 +10784,12 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
const int ne00, const int ne01,
const int nb00, const int nb01,
const int nb02, const int ne10,
const int ne11, const int nb10,
const int nb11, const int nb12,
const int ne02, const int nb00,
const int nb01, const int nb02,
const int nb03, const int ne10,
const int ne11, const int ne12,
const int nb10, const int nb11,
const int nb12, const int nb13,
dpct::queue_ptr stream) {
const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@ -10772,8 +10802,8 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, nb00, nb01,
nb02, ne10, ne11, nb10, nb11, nb12,
cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
});
}
@ -13910,19 +13940,23 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
GGML_ASSERT(src0->ne[3] == 1);
const int64_t ne02 = src0->ne[2];
const int64_t nb00 = src0->nb[0];
const int64_t nb01 = src0->nb[1];
const int64_t nb02 = src0->nb[2];
const int64_t nb03 = src0->nb[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
GGML_ASSERT(src1->ne[3] == 1);
const int64_t ne12 = src1->ne[2];
const int64_t nb10 = src1->nb[0];
const int64_t nb11 = src1->nb[1];
const int64_t nb12 = src1->nb[2];
const int64_t nb13 = src1->nb[3];
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
@ -13934,21 +13968,21 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index];
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));

2639
ggml-vulkan.cpp
View file

File diff suppressed because it is too large Load diff

23
ggml-vulkan.h
View file

@ -8,24 +8,29 @@ extern "C" {
#endif
#define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16
GGML_API void ggml_vk_init(void);
GGML_API void ggml_vk_init_cpu_assist(void);
GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node);
GGML_API void ggml_vk_preallocate_buffers(void);
GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node);
GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#ifdef GGML_VULKAN_CHECK_RESULTS
void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor);
void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#endif
GGML_API void ggml_vk_graph_cleanup(void);
GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
GGML_API void ggml_vk_free_cpu_assist(void);
// backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void);
GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void);
GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);

23
ggml.c
View file

@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
#elif defined(GGML_USE_CLBLAST)
ggml_cl_init();
#elif defined(GGML_USE_VULKAN)
ggml_vk_init();
ggml_vk_init_cpu_assist();
#elif defined(GGML_USE_SYCL)
ggml_init_sycl();
#endif
@ -2470,7 +2470,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
size_t max_size = 0;
for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) {
max_size = MAX(max_size, ggml_nbytes(tensor));
size_t bytes = ggml_nbytes(tensor);
max_size = MAX(max_size, bytes);
}
return max_size;
@ -11887,8 +11888,10 @@ GGML_CALL void ggml_rope_yarn_corr_dims(
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
) {
// start and end correction dims
dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)));
dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)));
float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
dims[0] = MAX(0, start);
dims[1] = MIN(n_dims - 1, end);
}
static void ggml_compute_forward_rope_f32(
@ -14847,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
#elif defined(GGML_USE_VULKAN)
const bool skip_cpu = ggml_vk_compute_forward(params, tensor);
const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
#ifdef GGML_VULKAN_CHECK_RESULTS
if (skip_cpu) {
ggml_vk_check_results_1(params, tensor);
ggml_vk_check_results_1_cpu_assist(params, tensor);
}
#endif
if (skip_cpu) {
@ -17266,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
#ifdef GGML_USE_VULKAN
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]);
ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]);
}
ggml_vk_preallocate_buffers();
ggml_vk_preallocate_buffers_cpu_assist();
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1);
ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1);
}
#endif
@ -17327,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
}
#ifdef GGML_USE_VULKAN
ggml_vk_graph_cleanup();
ggml_vk_graph_cleanup_cpu_assist();
#endif
// performance stats (graph)

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

@ -104,6 +104,7 @@ class MODEL_ARCH(IntEnum):
CODESHELL = auto()
ORION = auto()
INTERNLM2 = auto()
MINICPM = auto()
class MODEL_TENSOR(IntEnum):
@ -156,6 +157,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.CODESHELL: "codeshell",
MODEL_ARCH.ORION: "orion",
MODEL_ARCH.INTERNLM2: "internlm2",
MODEL_ARCH.MINICPM: "minicpm",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -464,6 +466,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.MINICPM: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
# TODO
}

267
llama.cpp
View file

@ -229,6 +229,7 @@ enum llm_arch {
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,
LLM_ARCH_MINICPM,
LLM_ARCH_UNKNOWN,
};
@ -252,6 +253,7 @@ static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
{ LLM_ARCH_INTERNLM2, "internlm2" },
{ LLM_ARCH_MINICPM, "minicpm" },
};
enum llm_kv {
@ -714,6 +716,29 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_MINICPM,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -1358,7 +1383,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
#elif defined(GGML_USE_CUBLAS)
buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type();
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
@ -1395,6 +1420,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
GGML_UNUSED(tensor_split);
}
static size_t llama_get_device_count() {
#if defined(GGML_USE_CUBLAS)
return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_VULKAN)
return ggml_backend_vk_get_device_count();
#else
return 1;
#endif
}
static size_t llama_get_device_memory(int device) {
#if defined(GGML_USE_CUBLAS)
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(device, &total, &free);
return free;
#elif defined(GGML_USE_VULKAN)
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(device, &total, &free);
return free;
#else
return 1;
GGML_UNUSED(device);
#endif
}
//
// globals
//
@ -1418,6 +1470,7 @@ enum e_model {
MODEL_UNKNOWN,
MODEL_0_5B,
MODEL_1B,
MODEL_2B,
MODEL_3B,
MODEL_4B,
MODEL_7B,
@ -1769,6 +1822,10 @@ struct llama_context {
ggml_backend_free(backend);
}
#ifdef GGML_USE_VULKAN
ggml_vk_free_cpu_assist();
#endif
ggml_backend_buffer_free(buf_input);
ggml_free(ctx_input);
}
@ -2794,6 +2851,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_1B: return "1B";
case MODEL_2B: return "2B";
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
@ -2933,6 +2991,13 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_MINICPM:
{
switch (hparams.n_layer) {
case 40: model.type = e_model::MODEL_2B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_FALCON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -3474,22 +3539,18 @@ static bool llm_load_tensors(
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
}
#ifdef GGML_USE_CUBLAS
if (split_mode == LLAMA_SPLIT_LAYER) {
// calculate the split points
int device_count = ggml_backend_cuda_get_device_count();
int device_count = llama_get_device_count();
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
float splits[GGML_CUDA_MAX_DEVICES];
std::vector<float> splits(device_count);
if (all_zero) {
// default split, by free memory
for (int i = 0; i < device_count; ++i) {
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(i, &total, &free);
splits[i] = free;
splits[i] = llama_get_device_memory(i);
}
} else {
std::copy(tensor_split, tensor_split + device_count, splits);
std::copy(tensor_split, tensor_split + device_count, splits.begin());
}
// sum and normalize the splits to get the split points
@ -3505,19 +3566,17 @@ static bool llm_load_tensors(
// assign the repeating layers to the devices according to the splits
int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits;
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
}
// assign the output layer
if (n_gpu_layers > n_layer) {
int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits;
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
} else
#endif
{
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
@ -3596,13 +3655,16 @@ static bool llm_load_tensors(
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
if (model.arch != LLM_ARCH_MINICPM){
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
}
for (int i = 0; i < n_layer; ++i) {
@ -6853,6 +6915,153 @@ struct llm_build_context {
return gf;
}
// ref: https://arxiv.org/abs/2203.03466
// https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
// based on the original build_llama() function
struct ggml_cgraph * build_minicpm() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
const int64_t n_embd = hparams.n_embd;
//TODO: if the model varies, these parameters need to be read from the model
const int64_t n_embd_base = 256;
const float scale_embd = 12.0f;
const float scale_depth = 1.4f;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
cb(inpL, "inp_embd", -1);
// scale the input embeddings
inpL = ggml_scale(ctx0, inpL, scale_embd);
cb(inpL, "inp_scaled", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
cb(inp_pos, "inp_pos", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
// scale_res - scale the hidden states for residual connection
const float scale_res = scale_depth/sqrtf(float(n_layer));
cur = ggml_scale(ctx0, cur, scale_res);
cb(cur, "hidden_scaled", -1);
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
// scale the hidden states for residual connection
cur = ggml_scale(ctx0, cur, scale_res);
cb(cur, "hidden_scaled_ffn", -1);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head scaling
const float scale_lmhead = float(n_embd_base)/float(n_embd);
cur = ggml_scale(ctx0, cur, scale_lmhead);
cb(cur, "lmhead_scaling", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
static struct ggml_cgraph * llama_build_graph(
@ -7015,6 +7224,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_internlm2();
} break;
case LLM_ARCH_MINICPM:
{
result = llm.build_minicpm();
} break;
default:
GGML_ASSERT(false);
}
@ -9778,8 +9991,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
@ -9818,9 +10031,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
}
//else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
// if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K;
//}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
@ -10816,13 +11029,15 @@ struct llama_context * llama_new_context_with_model(
}
#elif defined(GGML_USE_VULKAN)
if (model->n_gpu_layers > 0) {
ggml_backend_t backend = ggml_backend_vk_init();
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
llama_free(ctx);
return nullptr;
for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_vk_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
ctx->backends.push_back(backend);
}
#elif defined(GGML_USE_SYCL)
if (model->n_gpu_layers > 0) {

92
scripts/server-llm.sh
View file

@ -14,16 +14,17 @@
# - Might be unstable!
#
# Usage:
# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]
# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]
#
# --port: port number, default is 8888
# --repo: path to a repo containing GGUF model files
# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input
# --backend: cpu, cuda, metal, opencl, depends on the OS
# --gpu-id: gpu id, default is 0
# --n-parallel: number of parallel requests, default is 8
# --n-kv: KV cache size, default is 4096
# --verbose: verbose output
# --port: port number, default is 8888
# --repo: path to a repo containing GGUF model files
# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input
# --backend: cpu, cuda, metal, opencl, depends on the OS
# --gpu-id: gpu id, default is 0
# --n-parallel: number of parallel requests, default is 8
# --n-kv: KV cache size, default is 4096
# --verbose: verbose output
# --non-interactive: run without asking a permission to run
#
# Example:
#
@ -47,6 +48,7 @@ if ! command -v make &> /dev/null; then
fi
# parse arguments
is_interactive=1
port=8888
repo=""
wtype=""
@ -66,15 +68,16 @@ verbose=0
function print_usage {
printf "Usage:\n"
printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n"
printf " --port: port number, default is 8888\n"
printf " --repo: path to a repo containing GGUF model files\n"
printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n"
printf " --gpu-id: gpu id, default is 0\n"
printf " --n-parallel: number of parallel requests, default is 8\n"
printf " --n-kv: KV cache size, default is 4096\n"
printf " --verbose: verbose output\n\n"
printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n"
printf " --port: port number, default is 8888\n"
printf " --repo: path to a repo containing GGUF model files\n"
printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n"
printf " --gpu-id: gpu id, default is 0\n"
printf " --n-parallel: number of parallel requests, default is 8\n"
printf " --n-kv: KV cache size, default is 4096\n"
printf " --verbose: verbose output\n\n"
printf " --non-interactive: run without asking a permission to run\n"
printf "Example:\n\n"
printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n'
}
@ -82,6 +85,10 @@ function print_usage {
while [[ $# -gt 0 ]]; do
key="$1"
case $key in
--non-interactive)
is_interactive=0
shift
;;
--port)
port="$2"
shift
@ -176,31 +183,32 @@ repos=(
"https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF"
"https://huggingface.co/TheBloke/CausalLM-7B-GGUF"
)
if [ $is_interactive -eq 1 ]; then
printf "\n"
printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
printf " Based on the options that follow, the script might download a model file\n"
printf " from the internet, which can be a few GBs in size. The script will also\n"
printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n"
printf "\n"
printf " Upon success, an HTTP server will be started and it will serve the selected\n"
printf " model using llama.cpp for demonstration purposes.\n"
printf "\n"
printf " Please note:\n"
printf "\n"
printf " - All new data will be stored in the current folder\n"
printf " - The server will be listening on all network interfaces\n"
printf " - The server will run with default settings which are not always optimal\n"
printf " - Do not judge the quality of a model based on the results from this script\n"
printf " - Do not use this script to benchmark llama.cpp\n"
printf " - Do not use this script in production\n"
printf " - This script is only for demonstration purposes\n"
printf "\n"
printf " If you don't know what you are doing, please press Ctrl-C to abort now\n"
printf "\n"
printf " Press Enter to continue ...\n\n"
printf "\n"
printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
printf " Based on the options that follow, the script might download a model file\n"
printf " from the internet, which can be a few GBs in size. The script will also\n"
printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n"
printf "\n"
printf " Upon success, an HTTP server will be started and it will serve the selected\n"
printf " model using llama.cpp for demonstration purposes.\n"
printf "\n"
printf " Please note:\n"
printf "\n"
printf " - All new data will be stored in the current folder\n"
printf " - The server will be listening on all network interfaces\n"
printf " - The server will run with default settings which are not always optimal\n"
printf " - Do not judge the quality of a model based on the results from this script\n"
printf " - Do not use this script to benchmark llama.cpp\n"
printf " - Do not use this script in production\n"
printf " - This script is only for demonstration purposes\n"
printf "\n"
printf " If you don't know what you are doing, please press Ctrl-C to abort now\n"
printf "\n"
printf " Press Enter to continue ...\n\n"
read
read
fi
if [[ -z "$repo" ]]; then
printf "[+] No repo provided from the command line\n"