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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	CMakeLists.txt
#	Makefile
#	README.md
#	ci/run.sh
#	tests/test-tokenizer-0-falcon.cpp
#	tests/test-tokenizer-0-llama.cpp
#	tests/test-tokenizer-1-bpe.cpp
#	tests/test-tokenizer-1-llama.cpp
This commit is contained in:
Concedo 2024-02-17 15:22:05 +08:00
commit 8d5e25008f
60 changed files with 2568 additions and 735 deletions
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69
common/common.cpp
View file

@ -342,7 +342,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
const auto sampler_names = string_split(argv[i], ';');
sparams.samplers_sequence = sampler_types_from_names(sampler_names);
sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
} else if (arg == "--sampling-seq") {
if (++i >= argc) {
invalid_param = true;
@ -672,7 +672,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
} else if (arg == "--no-mmap") {
params.use_mmap = false;
} else if (arg == "--numa") {
params.numa = true;
if (++i >= argc) {
invalid_param = true;
break;
}
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; break; }
} else if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
} else if (arg == "--no-display-prompt") {
@ -936,7 +944,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -tb N, --threads-batch N\n");
printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n");
printf(" -td N, --threads-draft N");
printf(" number of threads to use during generation (default: same as --threads)");
printf(" number of threads to use during generation (default: same as --threads)\n");
printf(" -tbd N, --threads-batch-draft N\n");
printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
printf(" -p PROMPT, --prompt PROMPT\n");
@ -957,7 +965,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --samplers samplers that will be used for generation in the order, separated by \';\' (default: %s)\n", sampler_type_names.c_str());
printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n");
printf(" (default: %s)\n", sampler_type_names.c_str());
printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str());
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
@ -1006,7 +1015,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n");
printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks);
printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base");
printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base\n");
printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
@ -1023,7 +1032,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
if (llama_supports_mmap()) {
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
printf(" --numa attempt optimizations that help on some NUMA systems\n");
printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n");
printf(" - distribute: spread execution evenly over all nodes\n");
printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n");
printf(" - numactl: use the CPU map provided by numactl\n");
printf(" if run without this previously, it is recommended to drop the system page cache before using this\n");
printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n");
if (llama_supports_gpu_offload()) {
@ -1123,34 +1135,50 @@ std::vector<std::string> string_split(std::string input, char separator) {
return parts;
}
std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names) {
std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
{"top_k", llama_sampler_type::TOP_K},
{"top_p", llama_sampler_type::TOP_P},
{"typical_p", llama_sampler_type::TYPICAL_P},
{"min_p", llama_sampler_type::MIN_P},
{"tfs_z", llama_sampler_type::TFS_Z},
{"temperature", llama_sampler_type::TEMPERATURE}
};
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, llama_sampler_type> sampler_name_map {
{"top_k", llama_sampler_type::TOP_K},
std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
{"top-k", llama_sampler_type::TOP_K},
{"top_p", llama_sampler_type::TOP_P},
{"top-p", llama_sampler_type::TOP_P},
{"nucleus", llama_sampler_type::TOP_P},
{"typical_p", llama_sampler_type::TYPICAL_P},
{"typical-p", llama_sampler_type::TYPICAL_P},
{"typical", llama_sampler_type::TYPICAL_P},
{"min_p", llama_sampler_type::MIN_P},
{"min-p", llama_sampler_type::MIN_P},
{"tfs_z", llama_sampler_type::TFS_Z},
{"tfs-z", llama_sampler_type::TFS_Z},
{"tfs", llama_sampler_type::TFS_Z},
{"temp", llama_sampler_type::TEMP},
{"temperature", llama_sampler_type::TEMP}
{"temp", llama_sampler_type::TEMPERATURE}
};
std::vector<llama_sampler_type> sampler_types;
sampler_types.reserve(names.size());
for (const auto& name : names) {
const auto sampler_item = sampler_name_map.find(name);
if (sampler_item != sampler_name_map.end()) {
for (const auto & name : names)
{
auto sampler_item = sampler_canonical_name_map.find(name);
if (sampler_item != sampler_canonical_name_map.end())
{
sampler_types.push_back(sampler_item->second);
}
else
{
if (allow_alt_names)
{
sampler_item = sampler_alt_name_map.find(name);
if (sampler_item != sampler_alt_name_map.end())
{
sampler_types.push_back(sampler_item->second);
}
}
}
}
return sampler_types;
}
@ -1162,7 +1190,7 @@ std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & nam
{'y', llama_sampler_type::TYPICAL_P},
{'m', llama_sampler_type::MIN_P},
{'f', llama_sampler_type::TFS_Z},
{'t', llama_sampler_type::TEMP}
{'t', llama_sampler_type::TEMPERATURE}
};
std::vector<llama_sampler_type> sampler_types;
@ -1183,7 +1211,7 @@ std::string sampler_type_to_name_string(llama_sampler_type sampler_type) {
case llama_sampler_type::TYPICAL_P: return "typical_p";
case llama_sampler_type::TOP_P: return "top_p";
case llama_sampler_type::MIN_P: return "min_p";
case llama_sampler_type::TEMP: return "temp";
case llama_sampler_type::TEMPERATURE: return "temperature";
default : return "";
}
}
@ -1690,7 +1718,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);

4
common/common.h
View file

@ -70,6 +70,7 @@ struct gpt_params {
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
// sampling parameters
int32_t top_k = 40; // <= 0 to use vocab size
@ -148,7 +149,6 @@ struct gpt_params {
bool logits_all = false; // return logits for all tokens in the batch
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool numa = false; // attempt optimizations that help on some NUMA systems
bool verbose_prompt = false; // print prompt tokens before generation
bool display_prompt = true; // print prompt before generation
bool infill = false; // use infill mode
@ -179,7 +179,7 @@ void process_escapes(std::string& input);
// String utils
//
std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names);
std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
std::vector<std::string> string_split(std::string input, char separator);
std::string sampler_type_to_name_string(llama_sampler_type sampler_type);

2
common/sampling.cpp
View file

@ -139,7 +139,7 @@ static void sampler_queue(
case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case llama_sampler_type::TEMP:
case llama_sampler_type::TEMPERATURE:
if (dynatemp_range > 0) {
float dynatemp_min = std::max(0.0f, temp - dynatemp_range);
float dynatemp_max = std::max(0.0f, temp + dynatemp_range);

4
common/sampling.h
View file

@ -15,7 +15,7 @@ enum class llama_sampler_type : char {
MIN_P = 'm',
TFS_Z = 'f',
TYPICAL_P = 'y',
TEMP = 't'
TEMPERATURE = 't'
};
// sampling parameters
@ -46,7 +46,7 @@ typedef struct llama_sampling_params {
llama_sampler_type::TYPICAL_P,
llama_sampler_type::TOP_P,
llama_sampler_type::MIN_P,
llama_sampler_type::TEMP
llama_sampler_type::TEMPERATURE
};
std::string grammar; // optional BNF-like grammar to constrain sampling

136
convert-hf-to-gguf.py
View file

@ -10,7 +10,7 @@ import re
import sys
from enum import IntEnum
from pathlib import Path
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
import numpy as np
import torch
@ -25,15 +25,6 @@ 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):
for k in keys:
if k in d:
return d[k]
print(f"Could not find any of {keys}")
sys.exit()
###### MODEL DEFINITIONS ######
class SentencePieceTokenTypes(IntEnum):
@ -58,6 +49,15 @@ class Model:
self.hparams = Model.load_hparams(self.dir_model)
self.model_arch = self._get_model_architecture()
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
key = next((k for k in keys if k in self.hparams), None)
if key is not None:
return self.hparams[key]
if optional:
return None
raise KeyError(f"could not find any of: {keys}")
def set_vocab(self):
self._set_vocab_gpt2()
@ -79,28 +79,33 @@ class Model:
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.hparams.get(
"n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
))
if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
self.gguf_writer.add_block_count(self.block_count)
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
self.gguf_writer.add_context_length(n_ctx)
if (n_embd := self.hparams.get("hidden_size")) is not None:
n_embd = self.find_hparam(["hidden_size", "n_embd"])
self.gguf_writer.add_embedding_length(n_embd)
if (n_ff := self.hparams.get("intermediate_size")) is not None:
if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
if (n_head := self.hparams.get("num_attention_heads")) is not None:
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_head_count(n_head)
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None:
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
self.gguf_writer.add_file_type(self.ftype)
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
@ -211,6 +216,8 @@ class Model:
return MiniCPMModel
if model_architecture == "BertModel":
return BertModel
if model_architecture == "NomicBertModel":
return NomicBertModel
return Model
def _is_model_safetensors(self) -> bool:
@ -268,6 +275,8 @@ class Model:
return gguf.MODEL_ARCH.MINICPM
if arch == "BertModel":
return gguf.MODEL_ARCH.BERT
if arch == "NomicBertModel":
return gguf.MODEL_ARCH.NOMIC_BERT
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -1297,21 +1306,21 @@ class GPT2Model(Model):
class Phi2Model(Model):
def set_gguf_parameters(self):
block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
rot_pct = self.find_hparam(["partial_rotary_factor"])
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_name("Phi2")
self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"]))
self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(4 * n_embd)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"]))
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
@ -1636,19 +1645,34 @@ in chat mode so that the conversation can end normally.")
class BertModel(Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.block_count = self.hparams["num_hidden_layers"]
self.vocab_size = None
def set_gguf_parameters(self):
# TODO(cebtenzzre): merge with parent class
self.gguf_writer.add_name(self.dir_model.name)
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(self.block_count)
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
super().set_gguf_parameters()
self.gguf_writer.add_causal_attention(False)
self.gguf_writer.add_file_type(self.ftype)
# get pooling path
with open(self.dir_model / "modules.json", encoding="utf-8") as f:
modules = json.load(f)
pooling_path = None
for mod in modules:
if mod["type"] == "sentence_transformers.models.Pooling":
pooling_path = mod["path"]
break
# get pooling type
pooling_type = gguf.PoolingType.NONE
if pooling_path is not None:
with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
pooling = json.load(f)
if pooling["pooling_mode_mean_tokens"]:
pooling_type = gguf.PoolingType.MEAN
elif pooling["pooling_mode_cls_token"]:
pooling_type = gguf.PoolingType.CLS
else:
raise NotImplementedError("Only MEAN and CLS pooling types supported")
self.gguf_writer.add_pooling_type(pooling_type.value)
def set_vocab(self):
path = self.dir_model
@ -1658,6 +1682,7 @@ class BertModel(Model):
vocab = HfVocab(path, added_tokens_path)
tokens, scores, toktypes = zip(*vocab.all_tokens())
assert len(tokens) == vocab.vocab_size
self.vocab_size = vocab.vocab_size
# we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings
@ -1671,7 +1696,7 @@ class BertModel(Model):
if tok.startswith(b"##"):
return tok[2:]
return b"\xe2\x96\x81" + tok
tokens = [phantom(t, y) for t, y in zip(tokens, toktypes)]
tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
# set up bos and eos tokens (cls and sep)
self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
@ -1723,6 +1748,43 @@ class BertModel(Model):
self.gguf_writer.add_tensor(new_name, data)
class NomicBertModel(BertModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# the HF config claims n_ctx=8192, but it uses RoPE scaling
self.hparams["n_ctx"] = 2048
# SwigLU activation
assert self.hparams["activation_function"] == "swiglu"
# this doesn't do anything in the HF version
assert self.hparams["causal"] is False
# no bias tensors
assert self.hparams["qkv_proj_bias"] is False
assert self.hparams["mlp_fc1_bias"] is False
assert self.hparams["mlp_fc2_bias"] is False
# norm at end of layer
assert self.hparams["prenorm"] is False
# standard RoPE
assert self.hparams["rotary_emb_fraction"] == 1.0
assert self.hparams["rotary_emb_interleaved"] is False
assert self.hparams["rotary_emb_scale_base"] is None
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
def get_tensors(self):
assert self.vocab_size is not None
for name, data in super().get_tensors():
# Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
data = data[:self.vocab_size, :]
yield name, data
###### CONVERSION LOGIC ######

11
convert.py
View file

@ -1173,7 +1173,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
for (name, tensor) in model.items()}
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
@ -1199,7 +1199,11 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
for name, lazy_tensor in model.items():
tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
if name_new is None:
raise Exception(f"Unexpected tensor name: {name}")
if skip_unknown:
print(f"Unexpected tensor name: {name} - skipping")
continue
else:
raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
if tensor_type in should_skip:
print(f"skipping tensor {name_new}")
@ -1390,6 +1394,7 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY)
parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine")
parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
args = parser.parse_args(args_in)
if args.awq_path:
@ -1461,7 +1466,7 @@ def main(args_in: list[str] | None = None) -> None:
print(f"Special vocab info: {special_vocab}")
model = model_plus.model
model = convert_model_names(model, params)
model = convert_model_names(model, params, args.skip_unknown)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)

1
examples/CMakeLists.txt
View file

@ -38,6 +38,7 @@ else()
add_subdirectory(speculative)
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(gguf)
add_subdirectory(train-text-from-scratch)
add_subdirectory(imatrix)
if (LLAMA_BUILD_SERVER)

3
examples/batched-bench/batched-bench.cpp
View file

@ -82,7 +82,8 @@ int main(int argc, char ** argv) {
// init LLM
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model

2
examples/batched.swift/Sources/main.swift
View file

@ -17,7 +17,7 @@ let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(argu
let n_len: Int = 32
// init LLM
llama_backend_init(false)
llama_backend_init()
defer {
llama_backend_free()
}

3
examples/batched/batched.cpp
View file

@ -50,7 +50,8 @@ int main(int argc, char ** argv) {
// init LLM
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model

3
examples/beam-search/beam-search.cpp
View file

@ -119,7 +119,8 @@ int main(int argc, char ** argv)
// Init LLM :
//---------------------------------
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;

147
examples/embedding/embedding.cpp
View file

@ -8,6 +8,51 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static std::vector<std::string> split_lines(const std::string & s) {
std::string line;
std::vector<std::string> lines;
std::stringstream ss(s);
while (std::getline(ss, line)) {
lines.push_back(line);
}
return lines;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, false);
}
}
static void normalize(float * vec, float * out, int n) {
float norm = 0;
for (int i = 0; i < n; i++) {
norm += vec[i] * vec[i];
}
norm = sqrt(norm);
for (int i = 0; i < n; i++) {
out[i] = vec[i] / norm;
}
}
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
// run model
fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
if (llama_decode(ctx, batch) < 0) {
fprintf(stderr, "%s : failed to decode\n", __func__);
}
// normalize on copy
for (int k = 0; k < n_seq; k++) {
float * emb = llama_get_embeddings_ith(ctx, k);
float * out = output + k * n_embd;
normalize(emb, out, n_embd);
}
}
int main(int argc, char ** argv) {
gpt_params params;
@ -30,7 +75,8 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;
@ -56,59 +102,84 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s\n", get_system_info(params).c_str());
}
int n_past = 0;
// split the prompt into lines
std::vector<std::string> prompts = split_lines(params.prompt);
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch == params.n_ctx);
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true);
if (inp.size() > n_batch) {
inp.resize(n_batch);
}
inputs.push_back(inp);
}
// tokenization stats
if (params.verbose_prompt) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
for (int i = 0; i < (int) inputs.size(); i++) {
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) {
fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
}
fprintf(stderr, "\n\n");
}
fprintf(stderr, "\n");
}
if (embd_inp.size() > (size_t)n_ctx) {
fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n",
__func__, embd_inp.size(), n_ctx);
return 1;
}
while (!embd_inp.empty()) {
int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past += n_tokens;
embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);
}
// initialize batch
const int n_prompts = prompts.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
// allocate output
const int n_embd = llama_n_embd(model);
auto * embeddings = llama_get_embeddings(ctx);
std::vector<float> embeddings(n_prompts * n_embd, 0);
float * emb = embeddings.data();
// l2-normalize embeddings
float norm = 0;
for (int i = 0; i < n_embd; i++) {
norm += embeddings[i] * embeddings[i];
}
norm = sqrt(norm);
for (int i = 0; i < n_embd; i++) {
embeddings[i] /= norm;
// break into batches
int p = 0; // number of prompts processed already
int s = 0; // number of prompts in current batch
for (int k = 0; k < n_prompts; k++) {
// clamp to n_batch tokens
auto & inp = inputs[k];
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch);
p += s;
s = 0;
}
for (int i = 0; i < n_embd; i++) {
printf("%f ", embeddings[i]);
// add to batch
batch_add_seq(batch, inp, s);
s += 1;
}
printf("\n");
// final batch
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
// print first 3 embeddings
for (int j = 0; j < std::min(3, n_prompts); j++) {
fprintf(stderr, "embedding %d: ", j);
for (int i = 0; i < n_embd; i++) {
fprintf(stderr, "%f ", emb[j * n_embd + i]);
}
fprintf(stderr, "\n\n");
}
fprintf(stderr, "\n");
// clean up
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;

6
examples/finetune/README.md
View file

@ -80,9 +80,9 @@ The LORA rank can be configured for each model tensor type separately with these
--rank-wk N LORA rank for wk tensor (default 4)
--rank-wv N LORA rank for wv tensor (default 4)
--rank-wo N LORA rank for wo tensor (default 4)
--rank-w1 N LORA rank for w1 tensor (default 4)
--rank-w2 N LORA rank for w2 tensor (default 4)
--rank-w3 N LORA rank for w3 tensor (default 4)
--rank-ffn_gate N LORA rank for ffn_gate tensor (default 4)
--rank-ffn_down N LORA rank for ffn_down tensor (default 4)
--rank-ffn_up N LORA rank for ffn_up tensor (default 4)
```
The LORA rank of 'norm' tensors should always be 1.

234
examples/finetune/finetune.cpp
View file

@ -60,9 +60,9 @@ struct my_llama_layer {
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
};
struct my_llama_model {
@ -85,9 +85,9 @@ struct my_llama_lora_hparams {
uint32_t n_rank_wv = 4;
uint32_t n_rank_wo = 4;
uint32_t n_rank_ffn_norm = 1;
uint32_t n_rank_w1 = 4;
uint32_t n_rank_w2 = 4;
uint32_t n_rank_w3 = 4;
uint32_t n_rank_ffn_gate = 4;
uint32_t n_rank_ffn_down = 4;
uint32_t n_rank_ffn_up = 4;
uint32_t n_rank_tok_embeddings = 4;
uint32_t n_rank_norm = 1;
uint32_t n_rank_output = 4;
@ -117,12 +117,12 @@ struct my_llama_lora_layer {
struct ggml_tensor * ffn_norm_b;
// ff
struct ggml_tensor * w1_a;
struct ggml_tensor * w1_b;
struct ggml_tensor * w2_a;
struct ggml_tensor * w2_b;
struct ggml_tensor * w3_a;
struct ggml_tensor * w3_b;
struct ggml_tensor * ffn_gate_a;
struct ggml_tensor * ffn_gate_b;
struct ggml_tensor * ffn_down_a;
struct ggml_tensor * ffn_down_b;
struct ggml_tensor * ffn_up_a;
struct ggml_tensor * ffn_up_b;
};
struct my_llama_lora {
@ -208,9 +208,9 @@ static void print_lora_params(struct my_llama_lora_hparams * params) {
printf("%s: n_rank_wv : %u\n", __func__, params->n_rank_wv);
printf("%s: n_rank_wo : %u\n", __func__, params->n_rank_wo);
printf("%s: n_rank_ffn_norm : %u\n", __func__, params->n_rank_ffn_norm);
printf("%s: n_rank_w1 : %u\n", __func__, params->n_rank_w1);
printf("%s: n_rank_w2 : %u\n", __func__, params->n_rank_w2);
printf("%s: n_rank_w3 : %u\n", __func__, params->n_rank_w3);
printf("%s: n_rank_ffn_gate : %u\n", __func__, params->n_rank_ffn_gate);
printf("%s: n_rank_ffn_down : %u\n", __func__, params->n_rank_ffn_down);
printf("%s: n_rank_ffn_up : %u\n", __func__, params->n_rank_ffn_up);
printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
printf("%s: n_rank_norm : %u\n", __func__, params->n_rank_norm);
printf("%s: n_rank_output : %u\n", __func__, params->n_rank_output);
@ -319,9 +319,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model
layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
layer.w1 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
layer.w2 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
layer.w3 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
layer.ffn_gate = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
layer.ffn_down = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
layer.ffn_up = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
assert_shape_1d(layer.attention_norm, hparams.n_embd);
assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd);
@ -329,9 +329,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model
assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd_gqa());
assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd);
assert_shape_1d(layer.ffn_norm, hparams.n_embd);
assert_shape_2d(layer.w1, hparams.n_embd, hparams.n_ff);
assert_shape_2d(layer.w2, hparams.n_ff, hparams.n_embd);
assert_shape_2d(layer.w3, hparams.n_embd, hparams.n_ff);
assert_shape_2d(layer.ffn_gate, hparams.n_embd, hparams.n_ff);
assert_shape_2d(layer.ffn_down, hparams.n_ff, hparams.n_embd);
assert_shape_2d(layer.ffn_up, hparams.n_embd, hparams.n_ff);
}
}
@ -362,12 +362,12 @@ static void set_param_lora(struct my_llama_lora * lora) {
ggml_set_param(ctx, layer.wo_b);
ggml_set_param(ctx, layer.ffn_norm_a);
ggml_set_param(ctx, layer.ffn_norm_b);
ggml_set_param(ctx, layer.w1_a);
ggml_set_param(ctx, layer.w1_b);
ggml_set_param(ctx, layer.w2_a);
ggml_set_param(ctx, layer.w2_b);
ggml_set_param(ctx, layer.w3_a);
ggml_set_param(ctx, layer.w3_b);
ggml_set_param(ctx, layer.ffn_gate_a);
ggml_set_param(ctx, layer.ffn_gate_b);
ggml_set_param(ctx, layer.ffn_down_a);
ggml_set_param(ctx, layer.ffn_down_b);
ggml_set_param(ctx, layer.ffn_up_a);
ggml_set_param(ctx, layer.ffn_up_b);
}
}
@ -435,12 +435,12 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd);
layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff);
layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff);
layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd);
layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd);
layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff);
layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd);
layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff);
layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff);
layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd);
layer.ffn_up_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_embd);
layer.ffn_up_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_ff);
ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
@ -454,12 +454,12 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_b", i));
ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_a", i));
ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_b", i));
ggml_set_name(layer.w1_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i));
ggml_set_name(layer.w1_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i));
ggml_set_name(layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i));
ggml_set_name(layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i));
ggml_set_name(layer.w3_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i));
ggml_set_name(layer.w3_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i));
ggml_set_name(layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i));
ggml_set_name(layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i));
ggml_set_name(layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i));
ggml_set_name(layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i));
ggml_set_name(layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i));
ggml_set_name(layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i));
}
set_param_lora(lora);
@ -497,12 +497,12 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl
randomize_tensor_normal(layer.ffn_norm_a, rnd);
ggml_set_zero(layer.ffn_norm_b);
randomize_tensor_normal(layer.w1_a, rnd);
ggml_set_zero(layer.w1_b);
randomize_tensor_normal(layer.w2_a, rnd);
ggml_set_zero(layer.w2_b);
randomize_tensor_normal(layer.w3_a, rnd);
ggml_set_zero(layer.w3_b);
randomize_tensor_normal(layer.ffn_gate_a, rnd);
ggml_set_zero(layer.ffn_gate_b);
randomize_tensor_normal(layer.ffn_down_a, rnd);
ggml_set_zero(layer.ffn_down_b);
randomize_tensor_normal(layer.ffn_up_a, rnd);
ggml_set_zero(layer.ffn_up_b);
}
free_random_normal_distribution(rnd);
@ -614,9 +614,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b));
struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));
struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));
struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b));
struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b));
struct ggml_tensor * ffn_up = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b));
struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch);
struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch);
@ -659,11 +659,11 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch);
struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch);
struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch);
cur = t30;
if (enable_checkpointing) {
@ -723,9 +723,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f));
}
// allocating checkpoints in one block to reduce memory fragmentation
@ -798,9 +798,9 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
init_lora(model, lora);
@ -825,12 +825,12 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context
copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b));
copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
copy_tensor_by_name(layer.w1_a, f_ggml_ctx, ggml_get_name(layer.w1_a));
copy_tensor_by_name(layer.w1_b, f_ggml_ctx, ggml_get_name(layer.w1_b));
copy_tensor_by_name(layer.w2_a, f_ggml_ctx, ggml_get_name(layer.w2_a));
copy_tensor_by_name(layer.w2_b, f_ggml_ctx, ggml_get_name(layer.w2_b));
copy_tensor_by_name(layer.w3_a, f_ggml_ctx, ggml_get_name(layer.w3_a));
copy_tensor_by_name(layer.w3_b, f_ggml_ctx, ggml_get_name(layer.w3_b));
copy_tensor_by_name(layer.ffn_gate_a, f_ggml_ctx, ggml_get_name(layer.ffn_gate_a));
copy_tensor_by_name(layer.ffn_gate_b, f_ggml_ctx, ggml_get_name(layer.ffn_gate_b));
copy_tensor_by_name(layer.ffn_down_a, f_ggml_ctx, ggml_get_name(layer.ffn_down_a));
copy_tensor_by_name(layer.ffn_down_b, f_ggml_ctx, ggml_get_name(layer.ffn_down_b));
copy_tensor_by_name(layer.ffn_up_a, f_ggml_ctx, ggml_get_name(layer.ffn_up_a));
copy_tensor_by_name(layer.ffn_up_b, f_ggml_ctx, ggml_get_name(layer.ffn_up_b));
}
}
@ -868,9 +868,9 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_w1);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_w2);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_w3);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_ffn_gate);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_ffn_down);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_ffn_up);
gguf_add_tensor(fctx, lora->tok_embeddings_a);
gguf_add_tensor(fctx, lora->tok_embeddings_b);
@ -894,12 +894,12 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod
gguf_add_tensor(fctx, layer.wo_b);
gguf_add_tensor(fctx, layer.ffn_norm_a);
gguf_add_tensor(fctx, layer.ffn_norm_b);
gguf_add_tensor(fctx, layer.w1_a);
gguf_add_tensor(fctx, layer.w1_b);
gguf_add_tensor(fctx, layer.w2_a);
gguf_add_tensor(fctx, layer.w2_b);
gguf_add_tensor(fctx, layer.w3_a);
gguf_add_tensor(fctx, layer.w3_b);
gguf_add_tensor(fctx, layer.ffn_gate_a);
gguf_add_tensor(fctx, layer.ffn_gate_b);
gguf_add_tensor(fctx, layer.ffn_down_a);
gguf_add_tensor(fctx, layer.ffn_down_b);
gguf_add_tensor(fctx, layer.ffn_up_a);
gguf_add_tensor(fctx, layer.ffn_up_b);
}
}
@ -1104,12 +1104,12 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor
write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraB"));
write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraA"));
write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraB"));
write_tensor(&file, layer.w1_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA"));
write_tensor(&file, layer.w1_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB"));
write_tensor(&file, layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA"));
write_tensor(&file, layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB"));
write_tensor(&file, layer.w3_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA"));
write_tensor(&file, layer.w3_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB"));
write_tensor(&file, layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA"));
write_tensor(&file, layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB"));
write_tensor(&file, layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA"));
write_tensor(&file, layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB"));
write_tensor(&file, layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA"));
write_tensor(&file, layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB"));
}
}
@ -1139,9 +1139,9 @@ struct train_params {
uint32_t n_rank_wv;
uint32_t n_rank_wo;
uint32_t n_rank_ffn_norm;
uint32_t n_rank_w1;
uint32_t n_rank_w2;
uint32_t n_rank_w3;
uint32_t n_rank_ffn_gate;
uint32_t n_rank_ffn_down;
uint32_t n_rank_ffn_up;
uint32_t n_rank_tok_embeddings;
uint32_t n_rank_norm;
uint32_t n_rank_output;
@ -1152,9 +1152,9 @@ struct train_params {
bool custom_n_rank_wv;
bool custom_n_rank_wo;
bool custom_n_rank_ffn_norm;
bool custom_n_rank_w1;
bool custom_n_rank_w2;
bool custom_n_rank_w3;
bool custom_n_rank_ffn_gate;
bool custom_n_rank_ffn_down;
bool custom_n_rank_ffn_up;
bool custom_n_rank_tok_embeddings;
bool custom_n_rank_norm;
bool custom_n_rank_output;
@ -1186,9 +1186,9 @@ static struct train_params get_default_train_params() {
params.n_rank_wv = 4;
params.n_rank_wo = 4;
params.n_rank_ffn_norm = 1;
params.n_rank_w1 = 4;
params.n_rank_w2 = 4;
params.n_rank_w3 = 4;
params.n_rank_ffn_gate = 4;
params.n_rank_ffn_down = 4;
params.n_rank_ffn_up = 4;
params.n_rank_tok_embeddings = 4;
params.n_rank_norm = 1;
params.n_rank_output = 4;
@ -1199,9 +1199,9 @@ static struct train_params get_default_train_params() {
params.custom_n_rank_wv = false;
params.custom_n_rank_wo = false;
params.custom_n_rank_ffn_norm = false;
params.custom_n_rank_w1 = false;
params.custom_n_rank_w2 = false;
params.custom_n_rank_w3 = false;
params.custom_n_rank_ffn_gate = false;
params.custom_n_rank_ffn_down = false;
params.custom_n_rank_ffn_up = false;
params.custom_n_rank_tok_embeddings = false;
params.custom_n_rank_norm = false;
params.custom_n_rank_output = false;
@ -1232,9 +1232,9 @@ static void train_print_usage(int argc, char ** argv, const struct train_params
fprintf(stderr, " --rank-wk N LORA rank for wk tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wv N LORA rank for wv tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wo N LORA rank for wo tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w1 N LORA rank for w1 tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w2 N LORA rank for w2 tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w3 N LORA rank for w3 tensor, overrides default rank.\n");
fprintf(stderr, " --rank-ffn_gate N LORA rank for ffn_gate tensor, overrides default rank.\n");
fprintf(stderr, " --rank-ffn_down N LORA rank for ffn_down tensor, overrides default rank.\n");
fprintf(stderr, " --rank-ffn_up N LORA rank for ffn_up tensor, overrides default rank.\n");
print_common_train_usage(argc, argv, &params->common);
}
@ -1369,27 +1369,27 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par
}
params->n_rank_wo = std::stoi(argv[i]);
params->custom_n_rank_wo = true;
} else if (arg == "--rank-w1") {
} else if (arg == "--rank-ffn_gate") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w1 = std::stoi(argv[i]);
params->custom_n_rank_w1 = true;
} else if (arg == "--rank-w2") {
params->n_rank_ffn_gate = std::stoi(argv[i]);
params->custom_n_rank_ffn_gate = true;
} else if (arg == "--rank-ffn_down") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w2 = std::stoi(argv[i]);
params->custom_n_rank_w2 = true;
} else if (arg == "--rank-w3") {
params->n_rank_ffn_down = std::stoi(argv[i]);
params->custom_n_rank_ffn_down = true;
} else if (arg == "--rank-ffn_up") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w3 = std::stoi(argv[i]);
params->custom_n_rank_w3 = true;
params->n_rank_ffn_up = std::stoi(argv[i]);
params->custom_n_rank_ffn_up = true;
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
train_print_usage(argc, argv, &default_params);
@ -1452,12 +1452,12 @@ static int64_t get_parameter_count(struct my_llama_lora* lora) {
nx += ggml_nelements(layer.wo_b);
nx += ggml_nelements(layer.ffn_norm_a);
nx += ggml_nelements(layer.ffn_norm_b);
nx += ggml_nelements(layer.w1_a);
nx += ggml_nelements(layer.w1_b);
nx += ggml_nelements(layer.w2_a);
nx += ggml_nelements(layer.w2_b);
nx += ggml_nelements(layer.w3_a);
nx += ggml_nelements(layer.w3_b);
nx += ggml_nelements(layer.ffn_gate_a);
nx += ggml_nelements(layer.ffn_gate_b);
nx += ggml_nelements(layer.ffn_down_a);
nx += ggml_nelements(layer.ffn_down_b);
nx += ggml_nelements(layer.ffn_up_a);
nx += ggml_nelements(layer.ffn_up_b);
}
return nx;
}
@ -1511,9 +1511,9 @@ int main(int argc, char ** argv) {
uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r;
uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r;
uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1;
uint32_t n_rank_w1 = params.custom_n_rank_w1 ? params.n_rank_w1 : params.lora_r;
uint32_t n_rank_w2 = params.custom_n_rank_w2 ? params.n_rank_w2 : params.lora_r;
uint32_t n_rank_w3 = params.custom_n_rank_w3 ? params.n_rank_w3 : params.lora_r;
uint32_t n_rank_ffn_gate = params.custom_n_rank_ffn_gate ? params.n_rank_ffn_gate : params.lora_r;
uint32_t n_rank_ffn_down = params.custom_n_rank_ffn_down ? params.n_rank_ffn_down : params.lora_r;
uint32_t n_rank_ffn_up = params.custom_n_rank_ffn_up ? params.n_rank_ffn_up : params.lora_r;
uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1;
uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r;
@ -1523,9 +1523,9 @@ int main(int argc, char ** argv) {
lora.hparams.n_rank_wv = n_rank_wv;
lora.hparams.n_rank_wo = n_rank_wo;
lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm;
lora.hparams.n_rank_w1 = n_rank_w1;
lora.hparams.n_rank_w2 = n_rank_w2;
lora.hparams.n_rank_w3 = n_rank_w3;
lora.hparams.n_rank_ffn_gate = n_rank_ffn_gate;
lora.hparams.n_rank_ffn_down = n_rank_ffn_down;
lora.hparams.n_rank_ffn_up = n_rank_ffn_up;
lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
lora.hparams.n_rank_norm = n_rank_norm;
lora.hparams.n_rank_output = n_rank_output;
@ -1566,9 +1566,9 @@ int main(int argc, char ** argv) {
|| (lora.hparams.n_rank_wv != n_rank_wv)
|| (lora.hparams.n_rank_wo != n_rank_wo)
|| (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm)
|| (lora.hparams.n_rank_w1 != n_rank_w1)
|| (lora.hparams.n_rank_w2 != n_rank_w2)
|| (lora.hparams.n_rank_w3 != n_rank_w3)
|| (lora.hparams.n_rank_ffn_gate != n_rank_ffn_gate)
|| (lora.hparams.n_rank_ffn_down != n_rank_ffn_down)
|| (lora.hparams.n_rank_ffn_up != n_rank_ffn_up)
|| (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
|| (lora.hparams.n_rank_norm != n_rank_norm)
|| (lora.hparams.n_rank_output != n_rank_output)

3
examples/imatrix/imatrix.cpp
View file

@ -569,7 +569,8 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model_params mparams = llama_model_params_from_gpt_params(params);

3
examples/infill/infill.cpp
View file

@ -203,7 +203,8 @@ int main(int argc, char ** argv) {
std::mt19937 rng(params.seed);
LOG("%s: llama backend init\n", __func__);
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;

3
examples/llama-bench/llama-bench.cpp
View file

@ -1152,8 +1152,7 @@ int main(int argc, char ** argv) {
if (!params.verbose) {
llama_log_set(llama_null_log_callback, NULL);
}
bool numa = false;
llama_backend_init(numa);
llama_backend_init();
// initialize printer
std::unique_ptr<printer> p;

4
examples/llama.android/app/src/main/cpp/llama-android.cpp
View file

@ -274,8 +274,8 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) {
llama_backend_init(numa);
Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) {
llama_backend_init();
}
extern "C"

2
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -51,7 +51,7 @@ actor LlamaContext {
}
static func create_context(path: String) throws -> LlamaContext {
llama_backend_init(false)
llama_backend_init()
var model_params = llama_model_default_params()
#if targetEnvironment(simulator)

52
examples/llava/README.md
View file

@ -1,10 +1,12 @@
# LLaVA
Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants.
Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants,
as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants.
The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
models are available.
For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf)
After API is confirmed, more models will be supported / uploaded.
@ -18,10 +20,11 @@ After building, run: `./llava-cli` to see the usage. For example:
```
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
## Model conversion
## LLaVA 1.5
- Clone `llava-v15-7b` and `clip-vit-large-patch14-336` locally:
- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
```sh
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
@ -55,8 +58,49 @@ python ./convert.py ../llava-v1.5-7b
Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
## LLaVA 1.6 gguf conversion
1) Backup your pth/safetensor model files as llava-surgery modifies them
2) Use `python llava-surgery-v2.py -C -m /path/to/hf-model` which also supports llava-1.5 variants pytorch as well as safetensor models:
- you will find a llava.projector and a llava.clip file in your model directory
3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config_vit.json) and rename it to config.json.
4) Create the visual gguf model: `python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip-model-is-vision`
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
5) Everything else as usual: convert.py the hf model, quantize as needed
**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
**note** llava-1.6 greatly benefits from batched prompt processing (defaults work)
## llava-cli templating and llava-1.6 prompting
llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."`
For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system:
**For Mistral and using llava-cli binary:**
Add this: `-p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n"`
The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role
**For the 34B this should work:**
Add this: `-e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nProvide a full description.<|im_end|><|im_start|>assistant\n`
## How to know if you are running in llava-1.5 or llava-1.6 mode
When running llava-cli you will see a visual information right before the prompt is being processed:
**Llava-1.5:**
`encode_image_with_clip: image embedding created: 576 tokens`
**Llava-1.6 (anything above 576):**
`encode_image_with_clip: image embedding created: 2880 tokens`
Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6
## TODO
- [ ] Support non-CPU backend for the image encoding part.
- [x] Support non-CPU backend for the image encoding part.
- [ ] Support different sampling methods.
- [ ] Support more model variants.

612
examples/llava/clip.cpp
View file

@ -1,7 +1,7 @@
// NOTE: This is modified from clip.cpp only for LLaVA,
// so there might be still unnecessary artifacts hanging around
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
#include "ggml.h"
#include "ggml-alloc.h"
@ -30,6 +30,26 @@
#include <vector>
#include <sstream>
#include <cinttypes>
#include <limits>
//#define CLIP_DEBUG_FUNCTIONS
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
static std::string format(const char * fmt, ...) {
va_list ap;
@ -71,6 +91,11 @@ static std::string format(const char * fmt, ...) {
#define KEY_IMAGE_STD "clip.vision.image_std"
#define KEY_PROJ_TYPE "clip.projector_type"
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
//
// tensor name constants
//
@ -94,6 +119,7 @@ static std::string format(const char * fmt, ...) {
#define TN_LLAVA_PROJ "mm.%d.%s"
#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s"
#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
#define TN_IMAGE_NEWLINE "model.image_newline"
enum projector_type {
@ -165,7 +191,6 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int
}
}
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
std::string result;
for (size_t pos = 0; ; pos += search.length()) {
@ -233,31 +258,136 @@ static projector_type clip_projector_type_from_string(const std::string & name)
return PROJECTOR_TYPE_UNKNOWN;
}
//
// image data
//
#ifdef CLIP_DEBUG_FUNCTIONS
static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
std::cerr << "Failed to open file for writing: " << filename << std::endl;
return;
}
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
// PPM header: P6 format, width, height, and max color value
file << "P6\n" << img.nx << " " << img.ny << "\n255\n";
std::vector<uint8_t> buf;
// Write pixel data
for (size_t i = 0; i < img.buf.size(); i += 3) {
// PPM expects binary data in RGB format, which matches our image buffer
file.write(reinterpret_cast<const char*>(&img.buf[i]), 3);
}
file.close();
}
static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
std::cerr << "Failed to open file for writing: " << filename << std::endl;
return;
}
int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data
int bytesPerPixel = 3;
int widthInBytes = img.nx * bytesPerPixel;
int paddingAmount = (4 - (widthInBytes % 4)) % 4;
int stride = widthInBytes + paddingAmount;
// Bitmap file header
unsigned char fileHeader[14] = {
'B','M', // Signature
0,0,0,0, // Image file size in bytes
0,0,0,0, // Reserved
54,0,0,0 // Start of pixel array
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
// Total file size
fileSize = 54 + (stride * img.ny);
fileHeader[2] = (unsigned char)(fileSize);
fileHeader[3] = (unsigned char)(fileSize >> 8);
fileHeader[4] = (unsigned char)(fileSize >> 16);
fileHeader[5] = (unsigned char)(fileSize >> 24);
std::vector<float> buf;
// Bitmap information header (BITMAPINFOHEADER)
unsigned char infoHeader[40] = {
40,0,0,0, // Size of this header (40 bytes)
0,0,0,0, // Image width
0,0,0,0, // Image height
1,0, // Number of color planes
24,0, // Bits per pixel
0,0,0,0, // No compression
0,0,0,0, // Image size (can be 0 for no compression)
0,0,0,0, // X pixels per meter (not specified)
0,0,0,0, // Y pixels per meter (not specified)
0,0,0,0, // Total colors (color table not used)
0,0,0,0 // Important colors (all are important)
};
// Width and height in the information header
infoHeader[4] = (unsigned char)(img.nx);
infoHeader[5] = (unsigned char)(img.nx >> 8);
infoHeader[6] = (unsigned char)(img.nx >> 16);
infoHeader[7] = (unsigned char)(img.nx >> 24);
infoHeader[8] = (unsigned char)(img.ny);
infoHeader[9] = (unsigned char)(img.ny >> 8);
infoHeader[10] = (unsigned char)(img.ny >> 16);
infoHeader[11] = (unsigned char)(img.ny >> 24);
// Write file headers
file.write(reinterpret_cast<char*>(fileHeader), sizeof(fileHeader));
file.write(reinterpret_cast<char*>(infoHeader), sizeof(infoHeader));
// Pixel data
std::vector<unsigned char> padding(3, 0); // Max padding size to be added to each row
for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top
for (int x = 0; x < img.nx; ++x) {
// Each pixel
size_t pixelIndex = (y * img.nx + x) * 3;
unsigned char pixel[3] = {
img.buf[pixelIndex + 2], // BMP stores pixels in BGR format
img.buf[pixelIndex + 1],
img.buf[pixelIndex]
};
file.write(reinterpret_cast<char*>(pixel), 3);
}
// Write padding for the row
file.write(reinterpret_cast<char*>(padding.data()), paddingAmount);
}
file.close();
}
// debug function to convert f32 to u8
static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) {
dst.nx = src.nx;
dst.ny = src.ny;
dst.buf.resize(3 * src.nx * src.ny);
for (size_t i = 0; i < src.buf.size(); ++i) {
dst.buf[i] = static_cast<uint8_t>(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255));
}
}
#endif
//
// clip layers
//
struct clip_hparams {
int32_t image_size;
int32_t patch_size;
int32_t hidden_size;
int32_t n_intermediate;
int32_t projection_dim;
int32_t n_head;
int32_t n_layer;
float eps;
char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default)
int32_t image_grid_pinpoints[32];
int32_t image_crop_resolution;
};
struct clip_layer {
// attention
struct ggml_tensor * k_w;
@ -287,7 +417,7 @@ struct clip_layer {
};
struct clip_vision_model {
struct clip_vision_hparams hparams;
struct clip_hparams hparams;
// embeddings
struct ggml_tensor * class_embedding;
@ -310,6 +440,8 @@ struct clip_vision_model {
struct ggml_tensor * mm_2_w = NULL;
struct ggml_tensor * mm_2_b = NULL;
struct ggml_tensor * image_newline = NULL;
// Yi type models with mlp+normalization projection
struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4
struct ggml_tensor * mm_1_b = NULL;
@ -366,6 +498,7 @@ struct clip_ctx {
// memory buffers to evaluate the model
ggml_backend_buffer_t params_buffer = NULL;
ggml_backend_buffer_t compute_buffer = NULL;
ggml_backend_t backend = NULL;
ggml_gallocr_t compute_alloc = NULL;
};
@ -382,15 +515,16 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
const int image_size = hparams.image_size;
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
const int num_positions = num_patches + 1;
const int hidden_size = hparams.hidden_size;
const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head;
const int n_layer = hparams.n_layer;
//const int n_intermediate = hparams.n_intermediate;
//const int projection_dim = hparams.projection_dim;
const float eps = hparams.eps;
int batch_size = imgs->size;
const int batch_size = imgs->size;
if (ctx->has_llava_projector) {
GGML_ASSERT(batch_size == 1);
}
@ -540,7 +674,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
embeddings = ggml_gelu(ctx0, embeddings);
embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
@ -791,10 +924,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
if (idx != -1) {
const std::string proj_type = gguf_get_val_str(ctx, idx);
new_clip->proj_type = clip_projector_type_from_string(proj_type);
}
else {
} else {
new_clip->proj_type = PROJECTOR_TYPE_MLP;
}
if (new_clip->proj_type == PROJECTOR_TYPE_MLP) {
if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) {
new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM;
@ -920,11 +1053,41 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
try {
int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS);
int n = gguf_get_arr_n(ctx, idx);
const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx);
for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) {
hparams.image_grid_pinpoints[i] = pinpoints[i];
}
if (n < 32)
hparams.image_grid_pinpoints[n] = 0;
} catch (std::runtime_error & e) {
hparams.image_grid_pinpoints[0]=0;
}
try {
int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
} catch (std::runtime_error & e) {
strcpy(hparams.mm_patch_merge_type, "flat");
}
try {
hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
} catch(const std::exception& e) {
hparams.image_crop_resolution = hparams.image_size;
}
int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
int idx_std = get_key_idx(ctx, KEY_IMAGE_STD);
const float * mean_data = (const float *)gguf_get_arr_data(ctx, idx_mean);
const float * std_data = (const float *)gguf_get_arr_data(ctx, idx_std);
for (int i = 0; i < 3; ++i) {
new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));
new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std));
new_clip->image_mean[i] = mean_data[i];
new_clip->image_std[i] = std_data[i];
}
if (verbosity >= 2) {
@ -936,13 +1099,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
printf("v_projection_dim %d\n", hparams.projection_dim);
printf("v_n_head %d\n", hparams.n_head);
printf("v_n_layer %d\n", hparams.n_layer);
printf("v_eps %f\n", hparams.eps);
printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
printf("v_image_grid_pinpoints: ");
for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
printf("%d ", hparams.image_grid_pinpoints[i]);
}
printf("\n");
printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
}
try {
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
} catch(const std::exception& e) {
fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
}
// LLaVA projection
if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
@ -968,8 +1145,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
} catch (std::runtime_error & e) { }
}
else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
try {
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
// fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
} catch (std::runtime_error & e) { }
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
vision_model.mm_model_mlp_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias"));
@ -995,13 +1175,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
}
else {
} else {
std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
}
vision_model.layers.resize(hparams.n_layer);
for (int il = 0; il < hparams.n_layer; ++il) {
auto & layer = vision_model.layers[il];
layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight"));
@ -1052,6 +1232,18 @@ struct clip_image_f32 * clip_image_f32_init() {
void clip_image_u8_free(struct clip_image_u8 * img) { delete img; }
void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
void clip_image_u8_batch_free(struct clip_image_u8_batch & batch) {
if (batch.size > 0) {
delete[] batch.data;
batch.size = 0;
}
}
void clip_image_f32_batch_free(struct clip_image_f32_batch & batch) {
if (batch.size > 0) {
delete[] batch.data;
batch.size = 0;
}
}
static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
img->nx = nx;
@ -1084,24 +1276,252 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
return true;
}
// normalize: x = (x - mean) / std
// TODO: implement bicubic interpolation instead of linear.
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
// Linear interpolation between two points
inline float lerp(float s, float e, float t) {
return s + (e - s) * t;
}
// Bilinear resize function
static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) {
dst.nx = target_width;
dst.ny = target_height;
dst.buf.resize(3 * target_width * target_height);
float x_ratio = static_cast<float>(src.nx - 1) / target_width;
float y_ratio = static_cast<float>(src.ny - 1) / target_height;
for (int y = 0; y < target_height; y++) {
for (int x = 0; x < target_width; x++) {
float px = x_ratio * x;
float py = y_ratio * y;
int x_floor = static_cast<int>(px);
int y_floor = static_cast<int>(py);
float x_lerp = px - x_floor;
float y_lerp = py - y_floor;
for (int c = 0; c < 3; c++) {
float top = lerp(
static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
x_lerp
);
float bottom = lerp(
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
x_lerp
);
dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
}
}
}
}
// Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not
static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* dst, const float mean[3], const float std[3]) {
dst->nx = src->nx;
dst->ny = src->ny;
dst->buf.resize(src->buf.size());
for (size_t i = 0; i < src->buf.size(); ++i) {
int c = i % 3; // rgb
dst->buf[i] = (static_cast<float>(src->buf[i]) / 255.0f - mean[c]) / std[c];
}
}
inline float clip(float x, float lower, float upper) {
return std::max(lower, std::min(x, upper));
}
static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int target_width, int target_height) {
const int nx = img.nx;
const int ny = img.ny;
dst.nx = target_width;
dst.ny = target_height;
dst.buf.resize(3 * target_width * target_height);
float Cc;
float C[5];
float d0, d2, d3, a0, a1, a2, a3;
int i, j, k, jj;
int x, y;
float dx, dy;
float tx, ty;
tx = (float)nx / (float)target_width;
ty = (float)ny / (float)target_height;
// Bicubic interpolation; adapted from ViT.cpp, inspired from :
// -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36
// -> https://en.wikipedia.org/wiki/Bicubic_interpolation
for (i = 0; i < target_height; i++) {
for (j = 0; j < target_width; j++) {
x = (int)(tx * j);
y = (int)(ty * i);
dx = tx * j - x;
dy = ty * i - y;
for (k = 0; k < 3; k++) {
for (jj = 0; jj <= 3; jj++) {
d0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x - 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
d2 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
d3 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 2, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
a0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f);
dst.buf[(i * target_width + j) * 3 + k] = float(Cc2);
}
}
}
}
return true;
}
// llava-1.6 type of resize_and_pad (black)
static void resize_and_pad_image(const clip_image_u8& image, clip_image_u8 &image_output, const std::pair<int, int>& target_resolution) {
int target_width = target_resolution.first;
int target_height = target_resolution.second;
float scale_w = static_cast<float>(target_width) / image.nx;
float scale_h = static_cast<float>(target_height) / image.ny;
int new_width, new_height;
if (scale_w < scale_h) {
new_width = target_width;
new_height = std::min(static_cast<int>(std::ceil(image.ny * scale_w)), target_height);
} else {
new_height = target_height;
new_width = std::min(static_cast<int>(std::ceil(image.nx * scale_h)), target_width);
}
clip_image_u8 resized_image;
// bilinear_resize(image, resized_image, new_width, new_height);
bicubic_resize(image, resized_image, new_width, new_height);
clip_image_u8 padded_image;
padded_image.nx = target_width;
padded_image.ny = target_height;
padded_image.buf.resize(3 * target_width * target_height, 0); // Initialize with black
// Calculate padding offsets
int pad_x = (target_width - new_width) / 2;
int pad_y = (target_height - new_height) / 2;
// Copy the resized image into the center of the padded buffer
for (int y = 0; y < new_height; ++y) {
for (int x = 0; x < new_width; ++x) {
for (int c = 0; c < 3; ++c) {
padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c];
}
}
}
image_output = std::move(padded_image);
}
/**
* Selects the best resolution from a list of possible resolutions based on the original size.
*
* @param original_size The original size of the image in the format (width, height).
* @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
* @return The best fit resolution in the format (width, height).
*/
static std::pair<int, int> select_best_resolution(const std::pair<int, int> & original_size, const std::vector<std::pair<int, int>> & possible_resolutions) {
int original_width = original_size.first;
int original_height = original_size.second;
std::pair<int, int> best_fit;
int max_effective_resolution = 0;
int min_wasted_resolution = std::numeric_limits<int>::max();
for (const auto& resolution : possible_resolutions) {
int width = resolution.first;
int height = resolution.second;
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
int downscaled_width = static_cast<int>(original_width * scale);
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
best_fit = resolution;
}
}
return best_fit;
}
static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
std::vector<clip_image_u8*> patches;
int width = image.nx;
int height = image.ny;
for (int i = 0; i < height; i += patch_size) {
for (int j = 0; j < width; j += patch_size) {
clip_image_u8 *patch = clip_image_u8_init();
patch->nx = std::min(patch_size, width - j);
patch->ny = std::min(patch_size, height - i);
patch->buf.resize(3 * patch->nx * patch->ny);
for (int y = 0; y < patch->ny; ++y) {
for (int x = 0; x < patch->nx; ++x) {
for (int c = 0; c < 3; ++c) {
patch->buf[3 * (y * patch->nx + x) + c] = image.buf[3 * ((i + y) * width + (j + x)) + c];
}
}
}
patches.push_back(patch);
}
}
return patches;
}
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
// res_imgs memory is being allocated here, previous allocations will be freed if found
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) {
bool pad_to_square = true;
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
return false;
}
auto & params = ctx->vision_model.hparams;
// The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
if (strcmp(params.mm_patch_merge_type, "spatial_unpad") == 0) {
pad_to_square = false;
}
// free the previous res_imgs if any set
if (res_imgs.size > 0) {
clip_image_f32_batch_free(res_imgs);
}
res_imgs.data = nullptr;
res_imgs.size = 0;
// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
if (pad2square && img->nx != img->ny) {
if (pad_to_square && img->nx != img->ny) {
int longer_side = std::max(img->nx, img->ny);
temp->nx = longer_side;
temp->ny = longer_side;
temp->buf.resize(3 * longer_side * longer_side);
const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA (this is the mean rgb color * 255)
// fill with background color
for (size_t i = 0; i < temp->buf.size(); i++) {
@ -1118,19 +1538,64 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
temp->buf[j+2] = img->buf[i+2];
}
}
} else {
if (params.image_grid_pinpoints[0] != 0) {
// "spatial_unpad" with "anyres" processing for llava-1.6
std::vector<std::pair<int, int>> possible_resolutions;
for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
}
std::pair<int, int> best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions);
// clip_image_save_to_bmp(*img, "input.bmp");
resize_and_pad_image(*img, *temp, best_resolution); // we do not pad with mean-bg color anymore in llava-1.6
// clip_image_save_to_bmp(*temp, "resized.bmp");
// visually verify normalized image:
// normalize_image_u8_to_f32(*temp, *res, ctx->image_mean, ctx->image_std);
// {
// clip_image_u8 * temp2 = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*res, *temp2);
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32.bmp");
// clip_image_u8_free(temp2);
// }
std::vector<clip_image_u8 *> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
clip_image_u8 *image_original_resize = clip_image_u8_init();
// bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
patches.insert(patches.begin(), image_original_resize);
// clip_image_f32_batch_init(patches.size());
res_imgs.size = patches.size();
res_imgs.data = new clip_image_f32[res_imgs.size];
int num=0;
for (auto& patch : patches) {
normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std);
num++;
}
for (size_t i = 0; i < patches.size(); i++) {
// printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
clip_image_u8_free(patches[i]);
}
clip_image_u8_free(temp);
return true;
} else {
temp->nx = img->nx;
temp->ny = img->ny;
temp->buf.resize(img->buf.size());
memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
}
}
const int nx = temp->nx;
const int ny = temp->ny;
// clip_image_save_to_bmp(*temp, "resized_vanilla.bmp");
const int nx2 = ctx->vision_model.hparams.image_size;
const int ny2 = ctx->vision_model.hparams.image_size;
clip_image_f32 * res = clip_image_f32_init();
res->nx = nx2;
res->ny = ny2;
res->buf.resize(3 * nx2 * ny2);
@ -1184,9 +1649,26 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
}
clip_image_u8_free(temp);
// {
// clip_image_u8 * temp2 = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*res, *temp2);
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32_vanilla.bmp");
// clip_image_u8_free(temp2);
// }
// res_imgs.push_back(res);
res_imgs.size = 1;
res_imgs.data = new clip_image_f32[res_imgs.size];
res_imgs.data[0] = *res;
clip_image_f32_free(res);
return true;
}
ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
return ctx->vision_model.image_newline;
}
void clip_free(clip_ctx * ctx) {
ggml_free(ctx->ctx_data);
gguf_free(ctx->ctx_gguf);
@ -1194,6 +1676,42 @@ void clip_free(clip_ctx * ctx) {
delete ctx;
}
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
}
int32_t clip_image_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.image_size;
}
int32_t clip_patch_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.patch_size;
}
int32_t clip_hidden_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.hidden_size;
}
const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.mm_patch_merge_type;
}
const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.image_grid_pinpoints;
}
int clip_n_patches(const struct clip_ctx * ctx) {
const auto & params = ctx->vision_model.hparams;
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
n_patches /= 4;
}
return n_patches;
}
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
@ -1224,6 +1742,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// set inputs
const auto & model = ctx->vision_model;
const auto & hparams = model.hparams;
const int image_size = hparams.image_size;
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
@ -1301,11 +1820,11 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
return true;
}
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
ggml_type type = GGML_TYPE_Q4_1;
assert(itype < GGML_TYPE_COUNT);
@ -1494,26 +2013,13 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
}
else if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
return ctx->vision_model.mm_2_b->ne[0];
} else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
return ctx->vision_model.mm_3_b->ne[0];
}
else {
std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
}
}
int clip_n_patches(const struct clip_ctx * ctx) {
auto & params = ctx->vision_model.hparams;
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
n_patches /= 4;
}
return n_patches;
}
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
}

47
examples/llava/clip.h
View file

@ -24,25 +24,7 @@ struct clip_ctx;
extern "C" {
#endif
struct clip_vision_hparams {
int32_t image_size;
int32_t patch_size;
int32_t hidden_size;
int32_t n_intermediate;
int32_t projection_dim;
int32_t n_head;
int32_t n_layer;
float eps;
};
CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
CLIP_API void clip_free(struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
CLIP_API int clip_n_patches (const struct clip_ctx * ctx);
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
struct clip_ctx;
struct clip_image_u8_batch {
struct clip_image_u8 * data;
@ -54,18 +36,43 @@ struct clip_image_f32_batch {
size_t size;
};
CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity);
CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity);
CLIP_API void clip_free(struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
CLIP_API int32_t clip_hidden_size(const struct clip_ctx * ctx);
// TODO: should be enum, not string
CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
CLIP_API int clip_n_patches (const struct clip_ctx * ctx);
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
CLIP_API struct clip_image_u8 * clip_image_u8_init ();
CLIP_API struct clip_image_f32 * clip_image_f32_init();
CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch & batch);
CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch);
CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square);
/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);

66
examples/llava/convert-image-encoder-to-gguf.py
View file

@ -78,18 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp")
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
@ -105,7 +106,7 @@ if args.use_f32:
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision:
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
vocab = None
tokens = None
else:
@ -133,7 +134,7 @@ ftype = 1
if args.use_f32:
ftype = 0
if args.clip_model_is_vision:
if args.clip_model_is_vision or args.clip_model_is_openclip:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
@ -202,6 +203,57 @@ if has_vision_encoder:
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
# /**
# "image_grid_pinpoints": [
# [
# 336,
# 672
# ],
# [
# 672,
# 336
# ],
# [
# 672,
# 672
# ],
# [
# 1008,
# 336
# ],
# [
# 336,
# 1008
# ]
# ],
# Flattened:
# [
# 336, 672,
# 672, 336,
# 672, 672,
# 1008, 336,
# 336, 1008
# ]
# *
# */
if "image_grid_pinpoints" in v_hparams:
# flatten it
image_grid_pinpoints = []
for pinpoint in v_hparams["image_grid_pinpoints"]:
for p in pinpoint:
image_grid_pinpoints.append(p)
fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints)
if "image_crop_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"])
if "image_aspect_ratio" in v_hparams:
fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"])
if "image_split_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"])
if "mm_patch_merge_type" in v_hparams:
fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"])
if "mm_projector_type" in v_hparams:
fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"])
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean

29
examples/llava/llava-cli.cpp
View file

@ -155,11 +155,29 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
system_prompt = prompt.substr(0, image_pos);
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
printf("system_prompt: %s\n", system_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
printf("user_prompt: %s\n", user_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
} else {
// llava-1.5 native mode
system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
user_prompt = prompt + "\nASSISTANT:";
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
}
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
@ -171,13 +189,17 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
fprintf(stderr, "\n");
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
std::string response = "";
for (int i = 0; i < max_tgt_len; i++) {
const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
response += tmp;
if (strcmp(tmp, "</s>") == 0) break;
if (strstr(tmp, "###")) break; // Yi-VL behavior
printf("%s", tmp);
if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
fflush(stdout);
}
@ -196,7 +218,8 @@ static struct llava_context * llava_init(gpt_params * params) {
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_backend_init(params->numa);
llama_backend_init();
llama_numa_init(params->numa);
llama_model_params model_params = llama_model_params_from_gpt_params(*params);

167
examples/llava/llava-surgery-v2.py Normal file
View file

@ -0,0 +1,167 @@
import argparse
import glob
import os
import torch
from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file
# Function to determine if file is a SafeTensor file
def is_safetensor_file(file_path):
return file_path.endswith('.safetensors')
# Unified loading function
def load_model(file_path):
if is_safetensor_file(file_path):
tensors = {}
with safe_open(file_path, framework="pt", device="cpu") as f:
for key in f.keys():
tensors[key] = f.get_tensor(key).clone()
# output shape
print(f"{key} : {tensors[key].shape}")
return tensors, 'safetensor'
else:
return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch'
# Unified saving function
def save_model(model, file_path, file_type):
if file_type == 'safetensor':
# safe_save(model, file_path)
save_file(model, file_path)
else:
torch.save(model, file_path)
# Adapted function to clean vision tower from checkpoint
def clean_vision_tower_from_checkpoint(checkpoint_path):
checkpoint, file_type = load_model(checkpoint_path)
# file_type = 'pytorch'
model_path = os.path.dirname(checkpoint_path)
print(f"Searching for vision tower tensors in {checkpoint_path}")
clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))]
if len(clip_tensors) > 0:
print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}")
# Adapted for file type
clip_path = os.path.join(model_path, "llava.clip")
if os.path.exists(clip_path):
print(f"Loading existing llava.clip from {clip_path}")
existing_clip, _ = load_model(clip_path)
else:
print(f"Creating new llava.clip at {clip_path}")
existing_clip = {}
# Update existing_clip with new tensors, avoid duplicates
for name in clip_tensors:
simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name
print(f"Adding {simple_name} to llava.clip")
if simple_name not in existing_clip:
existing_clip[simple_name] = checkpoint[name]
# Save the updated clip tensors back to llava.clip
save_model(existing_clip, clip_path, 'pytorch')
# Remove the tensors from the original checkpoint
for name in clip_tensors:
del checkpoint[name]
# Save the updated checkpoint
checkpoint_path = checkpoint_path
save_model(checkpoint, checkpoint_path, file_type)
return True
return False
def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector):
newline_checkpoint_path = None
projector_checkpoint_path = None
for path in checkpoint_paths:
checkpoint, _ = load_model(path)
if newline_criteria(checkpoint) and newline_checkpoint_path is None:
newline_checkpoint_path = path
if projector(checkpoint):
projector_checkpoint_path = path
return newline_checkpoint_path, projector_checkpoint_path
def newline_criteria(checkpoint):
return any(k.startswith("model.image_newline") for k in checkpoint.keys())
def proj_criteria(checkpoint):
return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys())
# Command-line interface setup
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model")
ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files")
args = ap.parse_args()
if args.clean_vision_tower:
# Generalized to handle both PyTorch and SafeTensors models
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
# checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))]
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
for projector_checkpoint_path in checkpoint_paths:
print(f"Cleaning {projector_checkpoint_path}")
if not clean_vision_tower_from_checkpoint(projector_checkpoint_path):
print(f"No vision tower found in {projector_checkpoint_path}")
# we break once none is found, so far all models append them at the end
# break
print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.")
# Now we look for the projector in the last checkpoint
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
# last_checkpoint_path = checkpoint_paths[0]
# first_checkpoint_path = checkpoint_paths[-1]
newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria)
print(f"Taking projector from {projector_checkpoint_path}")
first_mm_tensors = []
first_checkpoint = None
if newline_checkpoint_path is not None:
print(f"Taking newline from {newline_checkpoint_path}")
first_checkpoint, file_type = load_model(newline_checkpoint_path)
first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")]
# Load the checkpoint
mm_tensors = []
last_checkpoint = None
if projector_checkpoint_path is not None:
last_checkpoint, file_type = load_model(projector_checkpoint_path)
mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")]
if len(mm_tensors) == 0:
if last_checkpoint is not None:
for k, v in last_checkpoint.items():
print(k)
print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.")
print("No tensors found. Is this a LLaVA model?")
exit()
print(f"Found {len(mm_tensors)} tensors to extract.")
print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
# projector = {name: checkpoint.[name].float() for name in mm_tensors}
projector = {}
for name in mm_tensors:
projector[name] = last_checkpoint[name].float()
for name in first_mm_tensors:
projector[name] = first_checkpoint[name].float()
if len(projector) > 0:
save_model(projector, f"{args.model}/llava.projector", 'pytorch')
for name in mm_tensors:
del last_checkpoint[name]
for name in first_mm_tensors:
del first_checkpoint[name]
if len(mm_tensors) > 0:
save_model(last_checkpoint, projector_checkpoint_path, file_type)
if len(first_mm_tensors) > 0:
save_model(first_checkpoint, newline_checkpoint_path, file_type)
print("Done!")
print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")

291
examples/llava/llava.cpp
View file

@ -2,31 +2,295 @@
#include "common.h"
#include "llama.h"
#include "llava.h"
#include "base64.hpp"
#include <cstdio>
#include <cstdlib>
#include <vector>
#include <numeric>
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
struct clip_image_grid_shape {
int first;
int second;
};
/**
* Selects the best resolution from a list of possible resolutions based on the original size.
*
* @param original_size The original size of the image in the format (width, height).
* @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
* @return The best fit resolution in the format (width, height).
*/
static std::pair<int, int> select_best_resolution(const std::pair<int, int>& original_size, const std::vector<std::pair<int, int>>& possible_resolutions) {
int original_width = original_size.first;
int original_height = original_size.second;
std::pair<int, int> best_fit;
int max_effective_resolution = 0;
int min_wasted_resolution = std::numeric_limits<int>::max();
for (const auto& resolution : possible_resolutions) {
int width = resolution.first;
int height = resolution.second;
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
int downscaled_width = static_cast<int>(original_width * scale);
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
best_fit = resolution;
}
}
return best_fit;
}
/**
* @brief Get the anyres image grid shape object
*
* @param image_size
* @param grid_pinpoints
* @param image_patch_size
* @return <int, int>
*/
static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<int, int> & image_size, const std::vector<std::pair<int, int>> & grid_pinpoints, int image_patch_size) {
/**
Conversion from gguf flat array to vector:
std::vector<std::pair<int, int>> possible_resolutions;
for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
}
*/
auto best_resolution = select_best_resolution(image_size, grid_pinpoints);
return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size};
}
// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) {
struct {
struct ggml_tensor * newline;
struct ggml_context * ctx;
} model;
const int32_t image_size = clip_image_size(ctx_clip);
const int32_t patch_size = clip_patch_size(ctx_clip);
int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
int num_patches_width = grid_shape.first; // grid 1-4
int num_patches_height = grid_shape.second; // grid 1-4
const size_t num_images = num_patches_width * num_patches_height + 1;
// TODO: size calculation is not calculated - it's only tens of MB
size_t ctx_size = 0;
{
ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features
ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32);
}
struct ggml_init_params params {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false, // NOTE: this should be false when using the legacy API
};
// Python reference code for full unpad:
/*
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
image_feature = torch.cat((
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1)
), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
*/
// We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval.
// In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet.
// Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them.
// Once all images are processed to prepended the base_image_features without any changes.
// Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling))
/*
image_feature = image_feature.view(2, 2, 24, 24, 4096)
image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
image_feature = image_feature.view(2, 24, 2, 24, 4096)
image_feature = image_feature.flatten(0, 3)
// Reshape to 4D tensor by merging the last two dimensions
image_feature = image_feature.view(2, 2, 24, 24*4096)
image_feature = image_feature.permute(0, 2, 1, 3).contiguous()
image_feature = image_feature.view(-1, 4096)
*/
model.ctx = ggml_init(params);
ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip);
model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
if (newline_tmp->backend != GGML_BACKEND_CPU) {
if (newline_tmp->buffer == NULL) {
printf("newline_tmp tensor buffer is NULL\n");
}
ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
} else {
model.newline->data = newline_tmp->data;
if (model.newline->data == NULL) {
printf("newline_tmp tensor data is NULL\n");
}
}
struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4
// ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
// fill it with the image embeddings, ignoring the base
for (size_t i = 1; i < num_images; i++) {
size_t offset = (i-1) * clip_embd_nbytes(ctx_clip);
memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip));
}
struct ggml_cgraph * gf = ggml_new_graph(model.ctx);
size_t size_ele = ggml_type_size(GGML_TYPE_F32);
struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features,
num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
num_patches_per_side,
num_patches_width,
num_patches_height,
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side,
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0);
// ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false);
struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3));
/**
At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings
image_feature = torch.cat((
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
), dim=-1)
*
*/
// ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false);
struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0);
// ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
ggml_build_forward_expand(gf, flatten);
ggml_graph_compute_with_ctx(model.ctx, gf, 1);
struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1];
memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
// append without newline tokens (default behavior in llava_arch when not using unpad ):
memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
*n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_patches(ctx_clip));
// Debug: Test single segments
// Current findings: sending base image, sending a segment embedding all works similar to python
// However, permuted embeddings do not work yet (stride issue?)
// memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context
// memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context
// *n_img_pos_out=576;
ggml_free(model.ctx);
return true;
}
#include "base64.hpp"
static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
clip_image_f32 * img_res = clip_image_f32_init();
if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
// std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
clip_image_f32_batch img_res_v;
img_res_v.size = 0;
img_res_v.data = nullptr;
if (!clip_image_preprocess(ctx_clip, img, img_res_v)) {
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
clip_image_f32_free(img_res);
delete[] img_res_v.data;
return false;
}
*n_img_pos = clip_n_patches(ctx_clip);
const int64_t t_img_enc_start_us = ggml_time_us();
bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
clip_image_f32_free(img_res);
const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
// flat / default llava-1.5 type embedding
*n_img_pos = clip_n_patches(ctx_clip);
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
delete[] img_res_v.data;
if (!encoded) {
fprintf(stderr, "Unable to encode image\n");
return false;
}
} else {
// spatial_unpad llava-1.6 type embedding
// TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
std::vector<float *> image_embd_v;
image_embd_v.resize(img_res_v.size);
for (size_t i = 0; i < img_res_v.size; i++) {
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
if (!encoded) {
fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
return false;
}
}
const int64_t t_img_enc_batch_us = ggml_time_us();
printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
const int32_t * image_grid = clip_image_grid(ctx_clip);
std::vector<std::pair<int, int>> grid_pinpoints;
for (int i = 0; i < 32 && image_grid[i] != 0; i += 2) {
grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
}
// free all img_res_v - not needed anymore
delete[] img_res_v.data;
img_res_v.size = 0;
img_res_v.data = nullptr;
const int32_t image_size = clip_image_size(ctx_clip);
struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
int n_img_pos_out;
clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
*n_img_pos = n_img_pos_out;
for (size_t i = 0; i < image_embd_v.size(); i++) {
free(image_embd_v[i]);
}
image_embd_v.clear();
// debug image/segment/normalization content:
// clip_image_u8 * tmp = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*image_feature, *tmp);
// clip_image_save_to_bmp(*tmp, "image_feature.bmp");
}
printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
const int64_t t_img_enc_end_us = ggml_time_us();
float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
@ -48,10 +312,9 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
}
static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
if (!image_embd) {
fprintf(stderr, "Unable to allocate memory for image embeddings\n");
free(image_embd);
return false;
}
@ -85,7 +348,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
return true;
}
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
clip_image_u8 * img = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
clip_image_u8_free(img);
@ -142,7 +405,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
return true;
}
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
unsigned char* image_bytes;
long image_bytes_length;
auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
@ -151,13 +414,13 @@ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct
return NULL;
}
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
free(image_bytes);
return embed;
}
LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) {
void llava_image_embed_free(struct llava_image_embed * embed) {
free(embed->embed);
free(embed);
}

2
examples/llava/llava.h
View file

@ -3,7 +3,6 @@
#include "ggml.h"
#ifdef LLAMA_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef LLAMA_BUILD
@ -42,7 +41,6 @@ LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
#ifdef __cplusplus
}
#endif

3
examples/lookahead/lookahead.cpp
View file

@ -54,7 +54,8 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;

3
examples/lookup/lookup.cpp
View file

@ -31,7 +31,8 @@ int main(int argc, char ** argv){
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;

6
examples/main/README.md
View file

@ -283,7 +283,11 @@ These options help improve the performance and memory usage of the LLaMA models.
### NUMA support
- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
- `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
### Memory Float 32

3
examples/main/main.cpp
View file

@ -186,7 +186,8 @@ int main(int argc, char ** argv) {
}
LOG("%s: llama backend init\n", __func__);
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;

3
examples/parallel/parallel.cpp
View file

@ -124,7 +124,8 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;

3
examples/passkey/passkey.cpp
View file

@ -71,7 +71,8 @@ int main(int argc, char ** argv) {
// init LLM
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model

3
examples/perplexity/perplexity.cpp
View file

@ -1810,7 +1810,8 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;

2
examples/quantize/quantize.cpp
View file

@ -238,7 +238,7 @@ int main(int argc, char ** argv) {
params.imatrix = &imatrix_data;
}
llama_backend_init(false);
llama_backend_init();
// parse command line arguments
const std::string fname_inp = argv[arg_idx];

9
examples/server/README.md
View file

@ -16,6 +16,13 @@ Command line options:
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
- `--numa STRATEGY`: Attempt one of the below optimization strategies that help on some NUMA systems
- `--numa distribute`: Spread execution evenly over all nodes
- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
- `--numa numactl`: Use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
- `--numa`: Attempt optimizations that help on some NUMA systems.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
@ -197,6 +204,8 @@ node index.js
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. (default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values)
### Result JSON
- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.

84
examples/server/server.cpp
View file

@ -437,10 +437,6 @@ struct llama_server_context
default_generation_settings_for_props["seed"] = -1;
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
// empty system prompt
system_prompt = "";
system_tokens.clear();
}
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
@ -677,6 +673,24 @@ struct llama_server_context
}
}
const auto &samplers_sequence = data.find("samplers");
if (samplers_sequence != data.end() && samplers_sequence->is_array())
{
std::vector<std::string> sampler_names;
for (const auto &sampler_name : *samplers_sequence)
{
if (sampler_name.is_string())
{
sampler_names.emplace_back(sampler_name);
}
}
slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
}
else
{
slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
}
if (multimodal)
{
const auto &images_data = data.find("image_data");
@ -766,12 +780,14 @@ struct llama_server_context
}
void update_system_prompt() {
kv_cache_clear();
system_tokens.clear();
if (!system_prompt.empty()) {
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
llama_batch_clear(batch);
kv_cache_clear();
for (int i = 0; i < (int)system_tokens.size(); ++i)
{
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
@ -788,6 +804,7 @@ struct llama_server_context
{
llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
}
}
LOG_TEE("system prompt updated\n");
system_need_update = false;
@ -808,11 +825,9 @@ struct llama_server_context
name_user = sys_props.value("anti_prompt", "");
name_assistant = sys_props.value("assistant_name", "");
if (slots.size() > 0)
{
notify_system_prompt_changed();
}
}
static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
const stop_type type, llama_client_slot &slot)
@ -969,18 +984,31 @@ struct llama_server_context
{
continue;
}
clip_image_f32 * img_res = clip_image_f32_init();
if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
clip_image_f32_batch img_res_v;
img_res_v.size = 0;
img_res_v.data = nullptr;
if (!clip_image_preprocess(clp_ctx, img.img_data, img_res_v))
{
LOG_TEE("Error processing the given image");
clip_free(clp_ctx);
clip_image_f32_batch_free(img_res_v);
return false;
}
if (img_res_v.size == 0)
{
LOG_TEE("Error processing the given image");
return false;
}
// note: assumes only one image was returned by clip_image_preprocess
clip_image_f32 * img_res = img_res_v.data;
img.image_tokens = clip_n_patches(clp_ctx);
img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
if (!img.image_embedding)
{
LOG_TEE("Unable to allocate memory for image embeddings\n");
clip_image_f32_batch_free(img_res_v);
clip_free(clp_ctx);
return false;
}
@ -988,9 +1016,12 @@ struct llama_server_context
if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
{
LOG_TEE("Unable to encode image\n");
clip_image_f32_batch_free(img_res_v);
return false;
}
clip_image_f32_free(img_res);
clip_image_f32_batch_free(img_res_v);
img.request_encode_image = false;
}
@ -1014,6 +1045,12 @@ struct llama_server_context
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
std::vector<std::string> samplers_sequence;
for (const auto &sampler_type : slot.sparams.samplers_sequence)
{
samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
}
return json {
{"n_ctx", slot.n_ctx},
{"model", params.model_alias},
@ -1044,6 +1081,7 @@ struct llama_server_context
{"logit_bias", slot.sparams.logit_bias},
{"n_probs", slot.sparams.n_probs},
{"grammar", slot.sparams.grammar},
{"samplers", samplers_sequence}
};
}
@ -1840,7 +1878,10 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
{
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
printf(" --numa attempt optimizations that help on some NUMA systems\n");
printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n");
printf(" - distribute: spread execution evenly over all nodes\n");
printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n");
printf(" - numactl: use the CPU map provided my numactl\n");
if (llama_supports_gpu_offload()) {
printf(" -ngl N, --n-gpu-layers N\n");
printf(" number of layers to store in VRAM\n");
@ -2249,9 +2290,17 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
{
params.use_mmap = false;
}
else if (arg == "--numa")
{
params.numa = true;
else if (arg == "--numa") {
if (++i >= argc) {
invalid_param = true;
break;
} else {
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; break; }
}
}
else if (arg == "--embedding")
{
@ -2482,7 +2531,8 @@ int main(int argc, char **argv)
params.model_alias = params.model;
}
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
{"commit", LLAMA_COMMIT}});

3
examples/simple/simple.cpp
View file

@ -31,7 +31,8 @@ int main(int argc, char ** argv) {
// init LLM
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model

3
examples/speculative/speculative.cpp
View file

@ -52,7 +52,8 @@ int main(int argc, char ** argv) {
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model_tgt = NULL;
llama_model * model_dft = NULL;

2
examples/tokenize/tokenize.cpp
View file

@ -17,7 +17,7 @@ int main(int argc, char ** argv) {
const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids";
llama_backend_init(false);
llama_backend_init();
llama_model_params model_params = llama_model_default_params();
model_params.vocab_only = true;

54
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -50,9 +50,9 @@ struct my_llama_layer {
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
struct ggml_tensor * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
};
struct my_llama_model {
@ -140,9 +140,9 @@ static void set_param_model(struct my_llama_model * model) {
ggml_set_param(ctx, layer.wv);
ggml_set_param(ctx, layer.wo);
ggml_set_param(ctx, layer.ffn_norm);
ggml_set_param(ctx, layer.w1);
ggml_set_param(ctx, layer.w2);
ggml_set_param(ctx, layer.w3);
ggml_set_param(ctx, layer.ffn_gate);
ggml_set_param(ctx, layer.ffn_down);
ggml_set_param(ctx, layer.ffn_up);
}
}
@ -198,9 +198,9 @@ static void init_model(struct my_llama_model * model) {
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
@ -211,9 +211,9 @@ static void init_model(struct my_llama_model * model) {
ggml_set_name(layer.ffn_norm, tni(LLM_TENSOR_FFN_NORM, i));
ggml_set_name(layer.w1, tni(LLM_TENSOR_FFN_GATE, i));
ggml_set_name(layer.w2, tni(LLM_TENSOR_FFN_DOWN, i));
ggml_set_name(layer.w3, tni(LLM_TENSOR_FFN_UP, i));
ggml_set_name(layer.ffn_gate, tni(LLM_TENSOR_FFN_GATE, i));
ggml_set_name(layer.ffn_down, tni(LLM_TENSOR_FFN_DOWN, i));
ggml_set_name(layer.ffn_up, tni(LLM_TENSOR_FFN_UP, i));
}
set_param_model(model);
@ -244,9 +244,9 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean,
randomize_tensor_normal(layer.ffn_norm, rnd);
randomize_tensor_normal(layer.w1, rnd);
randomize_tensor_normal(layer.w2, rnd);
randomize_tensor_normal(layer.w3, rnd);
randomize_tensor_normal(layer.ffn_gate, rnd);
randomize_tensor_normal(layer.ffn_down, rnd);
randomize_tensor_normal(layer.ffn_up, rnd);
}
free_random_normal_distribution(rnd);
@ -356,11 +356,11 @@ static struct ggml_tensor * llama_build_train_graphs(
struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, f_norm_rms_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
struct ggml_tensor * t23 = ggml_repeat (ctx, layer.ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch);
struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch);
struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch);
cur = t30;
checkpoints.push_back(cur);
@ -521,9 +521,9 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex
copy_tensor_by_name(layer.wv, f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
copy_tensor_by_name(layer.wo, f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
copy_tensor_by_name(layer.ffn_norm, f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
copy_tensor_by_name(layer.w1, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
copy_tensor_by_name(layer.w2, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
copy_tensor_by_name(layer.w3, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
copy_tensor_by_name(layer.ffn_gate, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
copy_tensor_by_name(layer.ffn_down, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
copy_tensor_by_name(layer.ffn_up, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
}
}
@ -664,9 +664,9 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo
gguf_add_tensor(fctx, layer.wv);
gguf_add_tensor(fctx, layer.wo);
gguf_add_tensor(fctx, layer.ffn_norm);
gguf_add_tensor(fctx, layer.w1);
gguf_add_tensor(fctx, layer.w2);
gguf_add_tensor(fctx, layer.w3);
gguf_add_tensor(fctx, layer.ffn_gate);
gguf_add_tensor(fctx, layer.ffn_down);
gguf_add_tensor(fctx, layer.ffn_up);
}
}
@ -915,9 +915,9 @@ static int64_t get_parameter_count(struct my_llama_model* model) {
nx += ggml_nelements(layer.wv);
nx += ggml_nelements(layer.wo);
nx += ggml_nelements(layer.ffn_norm);
nx += ggml_nelements(layer.w1);
nx += ggml_nelements(layer.w2);
nx += ggml_nelements(layer.w3);
nx += ggml_nelements(layer.ffn_gate);
nx += ggml_nelements(layer.ffn_down);
nx += ggml_nelements(layer.ffn_up);
}
return nx;
}

8
ggml-backend.c
View file

@ -219,6 +219,10 @@ GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void *
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
if (!size) {
return;
}
tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
}
@ -229,6 +233,10 @@ GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void *
GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
if (!size) {
return;
}
tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size);
}

1
ggml-cuda.cu
View file

@ -7943,6 +7943,7 @@ GGML_CALL void ggml_init_cublas() {
if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
initialized = true;
g_cublas_loaded = false;
fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__);
return;
}

73
ggml-vulkan.cpp
View file

@ -707,9 +707,21 @@ static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) {
q.cmd_buffer_idx = 0;
}
static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) {
static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
vk::MemoryType memory_type = mem_props->memoryTypes[i];
if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
(flags & memory_type.propertyFlags) == flags &&
mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
return static_cast<int32_t>(i);
}
}
return UINT32_MAX;
}
static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
#ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl;
std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
#endif
vk_buffer buf = std::make_shared<vk_buffer_struct>();
@ -736,15 +748,15 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz
uint32_t memory_type_index = UINT32_MAX;
for (uint32_t i = 0; i < mem_props.memoryTypeCount; ++i) {
vk::MemoryType memory_type = mem_props.memoryTypes[i];
if ((mem_req.memoryTypeBits & ((uint64_t)1 << i)) && (req_flags & memory_type.propertyFlags) == req_flags && mem_props.memoryHeaps[memory_type.heapIndex].size >= mem_req.size) {
memory_type_index = i;
break;
}
memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
buf->memory_property_flags = req_flags;
if (memory_type_index == UINT32_MAX && fallback_flags) {
memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
buf->memory_property_flags = fallback_flags;
}
if (memory_type_index >= mem_props.memoryTypeCount) {
if (memory_type_index == UINT32_MAX) {
ctx->device.lock()->device.destroyBuffer(buf->buffer);
buf->size = 0;
throw vk::OutOfDeviceMemoryError("No suitable memory type found");
@ -758,10 +770,9 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz
buf->size = 0;
throw e;
}
buf->memory_property_flags = req_flags;
buf->ptr = nullptr;
if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
}
@ -778,9 +789,9 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz
return buf;
}
static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) {
static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
try {
return ggml_vk_create_buffer(ctx, size, req_flags);
return ggml_vk_create_buffer(ctx, size, req_flags, fallback_flags);
} catch (const vk::SystemError& e) {
std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
@ -791,17 +802,17 @@ static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size
static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) {
vk_buffer buf;
try {
buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
} catch (const vk::SystemError& e) {
if (ctx->device.lock()->uma) {
// Fall back to host memory type
buf = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
} else {
buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
}
} catch (const vk::SystemError& e) {
std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
throw e;
}
}
return buf;
}
@ -1080,7 +1091,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
}
}
void ggml_vk_instance_init() {
static void ggml_vk_instance_init() {
if (vk_instance_initialized) {
return;
}
@ -1139,7 +1150,7 @@ void ggml_vk_instance_init() {
vk_instance_initialized = true;
}
void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
GGML_ASSERT(idx < vk_instance.device_indices.size());
size_t dev_num = vk_instance.device_indices[idx];
#ifdef GGML_VULKAN_DEBUG
@ -1422,7 +1433,9 @@ static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) {
#ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl;
#endif
vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached);
vk_buffer buf = ggml_vk_create_buffer(ctx, size,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
@ -1568,7 +1581,9 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect
static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
ggml_vk_destroy_buffer(ctx->sync_staging);
ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached);
ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
}
}
@ -4082,7 +4097,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
#endif
#if defined(GGML_VULKAN_RUN_TESTS)
ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached);
ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
ggml_vk_test_transfer(ctx, 8192 * 1000, false);
ggml_vk_test_transfer(ctx, 8192 * 1000, true);
@ -4174,7 +4191,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
if (ctx->staging != nullptr) {
ggml_vk_destroy_buffer(ctx->staging);
}
ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached);
ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
}
}
@ -4537,13 +4556,13 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
}
}
GGML_CALL int ggml_vk_get_device_count() {
GGML_CALL static int ggml_vk_get_device_count() {
ggml_vk_instance_init();
return vk_instance.device_indices.size();
}
GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
ggml_vk_instance_init();
std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
@ -4561,7 +4580,7 @@ void ggml_vk_init_cpu_assist() {
std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl;
for (size_t i = 0; i < ggml_vk_get_device_count(); i++) {
for (int i = 0; i < ggml_vk_get_device_count(); i++) {
ggml_vk_print_gpu_info(i);
}
// Initialize the first backend to make sure CPU matrix multiplications can be offloaded.
@ -5248,7 +5267,7 @@ GGML_CALL void ggml_backend_vk_get_device_description(int device, char * descrip
}
GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
GGML_ASSERT(device < vk_instance.device_indices.size());
GGML_ASSERT(device < (int) vk_instance.device_indices.size());
vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];

80
ggml.c
View file

@ -1954,9 +1954,16 @@ struct ggml_numa_node {
};
struct ggml_numa_nodes {
enum ggml_numa_strategy numa_strategy;
struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
uint32_t n_nodes;
uint32_t total_cpus; // hardware threads on system
uint32_t current_node; // node on which main process is execting
#ifdef __linux__
cpu_set_t cpuset; // cpuset from numactl
#else
uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype
#endif
};
//
@ -1990,7 +1997,22 @@ inline static void ggml_critical_section_end(void) {
atomic_fetch_sub(&g_state_barrier, 1);
}
void ggml_numa_init(void) {
#ifdef __linux__
static cpu_set_t ggml_get_numa_affinity(void) {
cpu_set_t cpuset;
pthread_t thread;
thread = pthread_self();
CPU_ZERO(&cpuset);
pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
return cpuset;
}
#else
static uint32_t ggml_get_numa_affinity(void) {
return 0; // no NUMA support
}
#endif
void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
if (g_state.numa.n_nodes > 0) {
fprintf(stderr, "ggml_numa_init: NUMA already initialized\n");
@ -2002,6 +2024,13 @@ void ggml_numa_init(void) {
char path[256];
int rv;
// set numa scheme
g_state.numa.numa_strategy = numa_flag;
GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy);
g_state.numa.cpuset = ggml_get_numa_affinity();
// enumerate nodes
while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) {
rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes);
@ -2020,11 +2049,17 @@ void ggml_numa_init(void) {
GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus);
if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) {
// figure out which node we're on
uint current_cpu;
int getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) {
g_state.numa.n_nodes = 0;
return;
}
GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu);
for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) {
struct ggml_numa_node * node = &g_state.numa.nodes[n];
GGML_PRINT_DEBUG("CPUs on node %u:", n);
@ -16638,26 +16673,46 @@ typedef pthread_t ggml_thread_t;
// Android's libc implementation "bionic" does not support setting affinity
#if defined(__linux__) && !defined(__BIONIC__)
static void set_numa_thread_affinity(int thread_n, int n_threads) {
static void set_numa_thread_affinity(int thread_n) {
if (!ggml_is_numa()) {
return;
}
// run thread on node_num thread_n / (threads per node)
const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes);
struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
int node_num;
int rv;
size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
switch(g_state.numa.numa_strategy) {
case GGML_NUMA_STRATEGY_DISTRIBUTE:
// run thread on node_num thread_n / (threads per node)
node_num = thread_n % g_state.numa.n_nodes;
break;
case GGML_NUMA_STRATEGY_ISOLATE:
// run thread on current_node
node_num = g_state.numa.current_node;
break;
case GGML_NUMA_STRATEGY_NUMACTL:
// use the cpuset that numactl gave us
rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv));
}
return;
default:
return;
}
struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
CPU_ZERO_S(setsize, cpus);
for (size_t i = 0; i < node->n_cpus; ++i) {
CPU_SET_S(node->cpus[i], setsize, cpus);
}
int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
strerror(rv));
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
}
CPU_FREE(cpus);
@ -16678,8 +16733,7 @@ static void clear_numa_thread_affinity(void) {
int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
strerror(rv));
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
}
CPU_FREE(cpus);
@ -16687,7 +16741,7 @@ static void clear_numa_thread_affinity(void) {
#else
// TODO: Windows etc.
// (the linux implementation may also work on BSD, someone should test)
static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); }
static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); }
static void clear_numa_thread_affinity(void) {}
#endif
@ -16987,7 +17041,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
const int n_threads = state->shared->n_threads;
set_numa_thread_affinity(state->ith, n_threads);
set_numa_thread_affinity(state->ith);
int node_n = -1;
int task_phase = GGML_TASK_FINALIZE;

12
ggml.h
View file

@ -665,6 +665,16 @@ extern "C" {
void * wdata;
};
// numa strategies
enum ggml_numa_strategy {
GGML_NUMA_STRATEGY_DISABLED = 0,
GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
GGML_NUMA_STRATEGY_ISOLATE = 2,
GGML_NUMA_STRATEGY_NUMACTL = 3,
GGML_NUMA_STRATEGY_MIRROR = 4,
GGML_NUMA_STRATEGY_COUNT
};
// misc
GGML_API void ggml_time_init(void); // call this once at the beginning of the program
@ -675,7 +685,7 @@ extern "C" {
GGML_API void ggml_print_backtrace(void);
GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems
GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
GGML_API void ggml_print_object (const struct ggml_object * obj);

45
gguf-py/examples/reader.py Normal file
View file

@ -0,0 +1,45 @@
#!/usr/bin/env python3
import sys
from pathlib import Path
from gguf.gguf_reader import GGUFReader
sys.path.insert(0, str(Path(__file__).parent.parent))
def read_gguf_file(gguf_file_path):
"""
Reads and prints key-value pairs and tensor information from a GGUF file in an improved format.
Parameters:
- gguf_file_path: Path to the GGUF file.
"""
reader = GGUFReader(gguf_file_path)
# List all key-value pairs in a columnized format
print("Key-Value Pairs:")
max_key_length = max(len(key) for key in reader.fields.keys())
for key, field in reader.fields.items():
value = field.parts[field.data[0]]
print(f"{key:{max_key_length}} : {value}")
print("----")
# List all tensors
print("Tensors:")
tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}"
print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization"))
print("-" * 80)
for tensor in reader.tensors:
shape_str = "x".join(map(str, tensor.shape))
size_str = str(tensor.n_elements)
quantization_str = tensor.tensor_type.name
print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str))
if __name__ == '__main__':
if len(sys.argv) < 2:
print("Usage: reader.py <path_to_gguf_file>")
sys.exit(1)
gguf_file_path = sys.argv[1]
read_gguf_file(gguf_file_path)

27
gguf-py/gguf/constants.py
View file

@ -40,6 +40,7 @@ class Keys:
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
POOLING_TYPE = "{arch}.pooling_type"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -72,6 +73,8 @@ class Keys:
UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_token_id"
CLS_ID = "tokenizer.ggml.cls_token_id"
MASK_ID = "tokenizer.ggml.mask_token_id"
ADD_BOS = "tokenizer.ggml.add_bos_token"
ADD_EOS = "tokenizer.ggml.add_eos_token"
ADD_PREFIX = "tokenizer.ggml.add_space_prefix"
@ -97,6 +100,7 @@ class MODEL_ARCH(IntEnum):
PERSIMMON = auto()
REFACT = auto()
BERT = auto()
NOMIC_BERT = auto()
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
@ -152,6 +156,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.PERSIMMON: "persimmon",
MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.NOMIC_BERT: "nomic-bert",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
@ -281,6 +286,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.LAYER_OUT_NORM,
],
MODEL_ARCH.NOMIC_BERT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
MODEL_TENSOR.TOKEN_TYPES,
MODEL_TENSOR.POS_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_OUT_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.LAYER_OUT_NORM,
],
MODEL_ARCH.MPT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
@ -542,6 +561,12 @@ class RopeScalingType(Enum):
YARN = 'yarn'
class PoolingType(IntEnum):
NONE = 0
MEAN = 1
CLS = 2
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
@ -668,5 +693,7 @@ KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID
KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID
KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID
KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID
KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID
KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID
KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON
KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV

10
gguf-py/gguf/gguf_writer.py
View file

@ -19,6 +19,7 @@ from .constants import (
GGUFValueType,
Keys,
RopeScalingType,
PoolingType,
TokenType,
)
@ -360,6 +361,9 @@ class GGUFWriter:
def add_causal_attention(self, value: bool) -> None:
self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value)
def add_rope_dimension_count(self, count: int) -> None:
self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
@ -411,6 +415,12 @@ class GGUFWriter:
def add_pad_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.PAD_ID, id)
def add_cls_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.CLS_ID, id)
def add_mask_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.MASK_ID, id)
def add_add_bos_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_BOS, value)

12
gguf-py/gguf/tensor_mapping.py
View file

@ -15,7 +15,7 @@ class TensorNameMap:
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert
"embeddings.word_embeddings", # bert nomic-bert
"language_model.embedding.word_embeddings", # persimmon
"wte", # gpt2
"transformer.embd.wte", # phi2
@ -24,13 +24,14 @@ class TensorNameMap:
# Token type embeddings
MODEL_TENSOR.TOKEN_TYPES: (
"embeddings.token_type_embeddings", # bert
"embeddings.token_type_embeddings", # bert nomic-bert
),
# Normalization of token embeddings
MODEL_TENSOR.TOKEN_EMBD_NORM: (
"word_embeddings_layernorm", # bloom
"embeddings.LayerNorm", # bert
"emb_ln", # nomic-bert
),
# Position embeddings
@ -103,6 +104,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.query_key_value", # persimmon
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
),
# Attention query
@ -152,11 +154,13 @@ class TensorNameMap:
"transformer.h.{bid}.mixer.out_proj", # phi2
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
"model.layers.{bid}.attention.wo", # internlm2
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
),
# Attention output norm
MODEL_TENSOR.ATTN_OUT_NORM: (
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"encoder.layers.{bid}.norm1", # nomic-bert
),
# Rotary embeddings
@ -205,6 +209,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.fc1", # phi2
"model.layers.layers.{bid}.mlp.up_proj", # plamo
"model.layers.{bid}.feed_forward.w3", # internlm2
"encoder.layers.{bid}.mlp.fc11", # nomic-bert
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -224,6 +229,7 @@ class TensorNameMap:
"transformer.h.{bid}.mlp.w2", # qwen
"model.layers.layers.{bid}.mlp.gate_proj", # plamo
"model.layers.{bid}.feed_forward.w1", # internlm2
"encoder.layers.{bid}.mlp.fc12", # nomic-bert
),
MODEL_TENSOR.FFN_GATE_EXP: (
@ -249,6 +255,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.fc2", # phi2
"model.layers.layers.{bid}.mlp.down_proj", # plamo
"model.layers.{bid}.feed_forward.w2", # internlm2
"encoder.layers.{bid}.mlp.fc2", # nomic-bert
),
MODEL_TENSOR.FFN_DOWN_EXP: (
@ -272,6 +279,7 @@ class TensorNameMap:
MODEL_TENSOR.LAYER_OUT_NORM: (
"encoder.layer.{bid}.output.LayerNorm", # bert
"encoder.layers.{bid}.norm2", # nomic-bert
)
}

6
gguf-py/gguf/vocab.py
View file

@ -29,7 +29,7 @@ class SpecialVocab:
if special_token_types is not None:
self.special_token_types = special_token_types
else:
self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad')
self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask')
self._load(Path(path))
def __repr__(self) -> str:
@ -152,10 +152,6 @@ class SpecialVocab:
add_entry = tokenizer_config.get(f'add_{typ}_token')
if isinstance(add_entry, bool):
self.add_special_token[typ] = add_entry
if not added_tokens:
# We will need this to get the content for the token, so if it's empty
# may as well just give up.
continue
entry = tokenizer_config.get(f'{typ}_token')
if isinstance(entry, str):
tc_content = entry

2
gpttype_adapter.cpp
View file

@ -921,7 +921,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
else if(file_format==FileFormat::GGUF_GENERIC)
{
llama_backend_init(false);
llama_backend_init();
llama_model_params model_params = llama_model_default_params();
llama_context_params llama_ctx_params = llama_context_default_params();

277
llama.cpp
View file

@ -221,6 +221,7 @@ enum llm_arch {
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
@ -246,6 +247,7 @@ static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_PERSIMMON, "persimmon" },
{ LLM_ARCH_REFACT, "refact" },
{ LLM_ARCH_BERT, "bert" },
{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" },
@ -278,6 +280,7 @@ enum llm_kv {
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_POOLING_TYPE,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@ -335,6 +338,7 @@ static std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@ -397,6 +401,7 @@ enum llm_tensor {
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_OUT_NORM,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM,
@ -409,6 +414,7 @@ enum llm_tensor {
LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
LLM_TENSOR_LAYER_OUT_NORM,
};
static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -574,12 +580,27 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_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_FFN_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_NOMIC_BERT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_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" },
},
@ -1511,6 +1532,7 @@ enum e_model {
MODEL_22M,
MODEL_33M,
MODEL_109M,
MODEL_137M,
MODEL_335M,
MODEL_0_5B,
MODEL_1B,
@ -1567,6 +1589,7 @@ struct llama_hparams {
float f_max_alibi_bias;
bool causal_attn = true;
uint32_t pooling_type = LLAMA_POOLING_NONE;
bool operator!=(const llama_hparams & other) const {
@ -1629,6 +1652,7 @@ struct llama_cparams {
bool mul_mat_q;
bool offload_kqv;
bool do_pooling;
ggml_backend_sched_eval_callback cb_eval;
void * cb_eval_user_data;
@ -1644,6 +1668,8 @@ struct llama_layer {
struct ggml_tensor * attn_q_norm_b;
struct ggml_tensor * attn_k_norm;
struct ggml_tensor * attn_k_norm_b;
struct ggml_tensor * attn_out_norm;
struct ggml_tensor * attn_out_norm_b;
// attention
struct ggml_tensor * wq;
@ -1662,6 +1688,8 @@ struct llama_layer {
// normalization
struct ggml_tensor * ffn_norm;
struct ggml_tensor * ffn_norm_b;
struct ggml_tensor * layer_out_norm;
struct ggml_tensor * layer_out_norm_b;
// ff
struct ggml_tensor * ffn_gate; // w1
@ -1928,7 +1956,8 @@ struct llama_context {
struct ggml_tensor * inp_pos; // I32 [n_batch]
struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch]
struct ggml_tensor * inp_K_shift; // I32 [n_ctx]
struct ggml_tensor * inp_sum; // F32 [1, n_batch]
struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch]
struct ggml_tensor * inp_cls; // I32 [n_batch]
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
@ -2897,6 +2926,11 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_22M: return "22M";
case MODEL_33M: return "33M";
case MODEL_109M: return "109M";
case MODEL_137M: return "137M";
case MODEL_0_5B: return "0.5B";
case MODEL_1B: return "1B";
case MODEL_2B: return "2B";
case MODEL_3B: return "3B";
@ -3099,6 +3133,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
switch (hparams.n_layer) {
case 3:
@ -3114,6 +3149,17 @@ static void llm_load_hparams(
model.type = e_model::MODEL_335M; break; // bge-large
}
} break;
case LLM_ARCH_NOMIC_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
model.type = e_model::MODEL_137M;
}
} break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -3374,7 +3420,12 @@ static void llm_load_vocab(
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
try {
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
} catch (const std::exception & e) {
LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
vocab.linefeed_id = vocab.special_pad_id;
}
} else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
vocab.linefeed_id = vocab.special_pad_id;
} else {
@ -3937,10 +3988,14 @@ static bool llm_load_tensors(
}
} break;
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
if (model.arch == LLM_ARCH_BERT) {
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
}
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
@ -3950,12 +4005,7 @@ static bool llm_load_tensors(
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
if (model.arch == LLM_ARCH_BERT) {
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
@ -3964,15 +4014,29 @@ static bool llm_load_tensors(
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
} else {
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
}
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
if (model.arch == LLM_ARCH_BERT) {
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
} else {
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
}
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
}
} break;
case LLM_ARCH_BLOOM:
@ -4451,9 +4515,21 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
model.hparams.vocab_only = params.vocab_only;
try {
llm_load_arch(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
}
try {
llm_load_hparams(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
}
try {
llm_load_vocab(ml, model);
} catch(const std::exception & e) {
throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
}
llm_load_print_meta(ml, model);
@ -4931,7 +5007,7 @@ struct llm_build_context {
const int32_t n_orig_ctx;
const bool do_rope_shift;
const bool causal_attn;
const uint32_t pooling_type;
const llm_build_cb & cb;
@ -4975,7 +5051,7 @@ struct llm_build_context {
kv_head (worst_case ? n_ctx - n_tokens : kv_self.head),
n_orig_ctx (cparams.n_yarn_orig_ctx),
do_rope_shift (worst_case || kv_self.has_shift),
causal_attn (hparams.causal_attn),
pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE),
cb (cb),
buf_compute_meta (lctx.buf_compute_meta) {
// all initializations should be done in init()
@ -5823,22 +5899,27 @@ struct llm_build_context {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
// get input vectors with right size
const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type);
struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
struct ggml_tensor * inp_sum = ggml_view_1d(ctx0, lctx.inp_sum, n_tokens, 0);
struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0);
struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0);
// construct input embeddings (token, type, position)
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
// token types are hardcoded to zero ("Sentence A")
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
inpL = ggml_add(ctx0, inpL, type_row0);
if (model.arch == LLM_ARCH_BERT) {
inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
}
cb(inpL, "inp_embd", -1);
// embed layer norm
@ -5854,7 +5935,7 @@ struct llm_build_context {
struct ggml_tensor * cur = inpL;
// self-attention
{
if (model.arch == LLM_ARCH_BERT) {
struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
cb(Qcur, "Qcur", il);
@ -5867,6 +5948,37 @@ struct llm_build_context {
// seems like we just need to do this for Q?
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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);
} else {
// compute Q and K and RoPE them
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
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, 2, 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, 2, 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);
@ -5877,25 +5989,34 @@ struct llm_build_context {
cur = ggml_add(ctx0, cur, inpL);
// attention layer norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, cb, il);
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
struct ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
if (model.arch == LLM_ARCH_BERT) {
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
} else {
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);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
// output layer norm
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, cb, il);
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
// input for next layer
inpL = cur;
@ -5904,9 +6025,15 @@ struct llm_build_context {
// final output
cur = inpL;
// pooling
cur = ggml_mul_mat(ctx0, inp_sum, ggml_cont(ctx0, ggml_transpose(ctx0, cur)));
cb(cur, "result_embed", -1);
// pooling layer
if (pooling_type == LLAMA_POOLING_MEAN) {
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
} else if (pooling_type == LLAMA_POOLING_CLS) {
cur = ggml_get_rows(ctx0, cur, inp_cls);
} else {
GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type");
}
cb(cur, "result_embd", -1);
ggml_build_forward_expand(gf, cur);
@ -7336,6 +7463,7 @@ static struct ggml_cgraph * llama_build_graph(
result = llm.build_refact();
} break;
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
{
result = llm.build_bert();
} break;
@ -7439,7 +7567,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
for (int i = 0; i < n_kv; ++i) {
float f;
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) ||
(hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) {
f = -INFINITY;
} else {
f = 0;
@ -7450,16 +7579,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
{
assert(ggml_backend_buffer_is_host(lctx.inp_sum->buffer));
float * data = (float *) lctx.inp_sum->data;
for (int i = 0; i < batch.n_tokens; ++i) {
data[i] = 1.0f/float(batch.n_tokens);
}
}
if (kv_self.has_shift) {
const int64_t n_ctx = cparams.n_ctx;
@ -7471,6 +7590,49 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
data[i] = lctx.kv_self.cells[i].delta;
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
float * data = (float *) lctx.inp_mean->data;
memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
std::vector<uint64_t> sum(n_tokens, 0);
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
sum[seq_id] += 1;
}
std::vector<float> div(n_tokens, 0.0f);
for (int i = 0; i < n_tokens; ++i) {
const uint64_t s = sum[i];
if (s > 0) {
div[i] = 1.0f/float(s);
}
}
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
data[seq_id*n_tokens + i] = div[seq_id];
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
uint32_t * data = (uint32_t *) lctx.inp_cls->data;
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
const llama_pos pos = batch.pos[i];
if (pos == 0) {
data[seq_id] = i;
}
}
}
}
// decode a batch of tokens by evaluating the transformer
@ -7582,7 +7744,7 @@ static int llama_decode_internal(
embeddings = gf->nodes[gf->n_nodes - 3];
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
}
} else if (strcmp(res->name, "result_embed") == 0) {
} else if (strcmp(res->name, "result_embd") == 0) {
embeddings = res;
res = nullptr;
} else {
@ -7705,11 +7867,12 @@ static int llama_decode_internal(
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
const int64_t embed_pos = res ? n_embd * (n_tokens-1) : 0;
const int64_t embd_pos = res ? n_embd * (n_tokens-1) : 0;
const int64_t embd_size = res ? n_embd : n_embd * n_tokens;
embedding_out.resize(n_embd);
embedding_out.resize(embd_size);
ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings);
ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embed_pos*sizeof(float), n_embd*sizeof(float));
ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float));
ggml_backend_synchronize(embeddings_backend);
}
@ -7787,7 +7950,13 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
switch (llama_vocab_get_type(vocab)) {
case LLAMA_VOCAB_TYPE_SPM: {
const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
return vocab.token_to_id.at(buf);
auto token = vocab.token_to_id.find(buf);
if (token != vocab.token_to_id.end()) {
return (*token).second;
}
// Try to fall back to just the byte as a string
const char buf2[2] = { (char)ch, 0 };
return vocab.token_to_id.at(buf2);
}
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_BPE: {
@ -7911,6 +8080,7 @@ private:
if (p == rev_merge.end()) {
// output any symbols that did not form tokens as bytes.
output.reserve(output.size() + symbol.n);
for (int j = 0; j < (int)symbol.n; ++j) {
llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
output.push_back(token_id);
@ -8693,6 +8863,7 @@ struct fragment_buffer_variant {
raw_text(_dummy),
offset(0),
length(0) {}
fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
:
type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
@ -10767,7 +10938,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
quantize &= !params->only_copy;
// do not quantize expert gating tensors
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_FFN_GATE_INP, "weight");
// do not quantize positional embeddings and token types (BERT)
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
enum ggml_type new_type;
void * new_data;
@ -11041,7 +11216,7 @@ static int llama_apply_lora_from_file_internal(
{
LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
__func__, ftype);
return false;
return 1;
}
}
@ -11269,6 +11444,7 @@ struct llama_context_params llama_context_default_params() {
/*.logits_all =*/ false,
/*.embedding =*/ false,
/*.offload_kqv =*/ true,
/*.do_pooling =*/ true,
};
return result;
@ -11319,7 +11495,7 @@ bool llama_mlock_supported(void) {
return llama_supports_mlock();
}
void llama_backend_init(bool numa) {
void llama_backend_init(void) {
ggml_time_init();
// needed to initialize f16 tables
@ -11329,15 +11505,17 @@ void llama_backend_init(bool numa) {
ggml_free(ctx);
}
if (numa) {
ggml_numa_init();
}
#ifdef GGML_USE_MPI
ggml_mpi_backend_init();
#endif
}
void llama_numa_init(enum ggml_numa_strategy numa) {
if (numa != GGML_NUMA_STRATEGY_DISABLED) {
ggml_numa_init(numa);
}
}
void llama_backend_free(void) {
#ifdef GGML_USE_MPI
ggml_mpi_backend_free();
@ -11414,6 +11592,7 @@ struct llama_context * llama_new_context_with_model(
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.mul_mat_q = params.mul_mat_q;
cparams.offload_kqv = params.offload_kqv;
cparams.do_pooling = params.do_pooling;
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
@ -11579,14 +11758,16 @@ struct llama_context * llama_new_context_with_model(
ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch);
ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx);
ctx->inp_sum = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, 1, cparams.n_batch);
ctx->inp_mean = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_batch, cparams.n_batch);
ctx->inp_cls = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
ggml_set_name(ctx->inp_tokens, "inp_tokens");
ggml_set_name(ctx->inp_embd, "inp_embd");
ggml_set_name(ctx->inp_pos, "inp_pos");
ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask");
ggml_set_name(ctx->inp_K_shift, "inp_K_shift");
ggml_set_name(ctx->inp_sum, "inp_sum");
ggml_set_name(ctx->inp_mean, "inp_mean");
ggml_set_name(ctx->inp_cls, "inp_cls");
ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true));
@ -12447,6 +12628,10 @@ float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
}
float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
return ctx->embedding.data() + i*ctx->model.hparams.n_embd;
}
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
return model->vocab.id_to_token[token].text.c_str();
}

16
llama.h
View file

@ -112,6 +112,12 @@ extern "C" {
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
};
enum llama_pooling_type {
LLAMA_POOLING_NONE = 0,
LLAMA_POOLING_MEAN = 1,
LLAMA_POOLING_CLS = 2,
};
enum llama_split_mode {
LLAMA_SPLIT_NONE = 0, // single GPU
LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs
@ -236,6 +242,7 @@ extern "C" {
bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool embedding; // embedding mode only
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
};
// model quantization parameters
@ -305,7 +312,10 @@ extern "C" {
// Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations
// Call once at the start of the program
LLAMA_API void llama_backend_init(bool numa);
LLAMA_API void llama_backend_init(void);
//optional:
LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void);
@ -630,6 +640,10 @@ extern "C" {
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Get the embeddings for the ith sequence
// llama_get_embeddings(ctx) + i*n_embd
LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
//
// Vocab
//

37
scripts/compare-commits.sh Executable file
View file

@ -0,0 +1,37 @@
#!/bin/bash
if [ $# -lt 2 ]; then
echo "usage: ./scripts/compare-commits.sh <commit1> <commit2> [additional llama-bench arguments]"
exit 1
fi
set -e
set -x
bench_args="${@:3}"
rm -f llama-bench.sqlite
backend="cpu"
if [[ "$OSTYPE" == "darwin"* ]]; then
backend="metal"
elif command -v nvcc &> /dev/null; then
backend="cuda"
fi
make_opts=""
if [[ "$backend" == "cuda" ]]; then
make_opts="LLAMA_CUBLAS=1"
fi
git checkout $1
make clean && make -j32 $make_opts llama-bench
./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
git checkout $2
make clean && make -j32 $make_opts llama-bench
./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
./scripts/compare-llama-bench.py -b $1 -c $2

107
scripts/hf.sh Executable file
View file

@ -0,0 +1,107 @@
#!/bin/bash
#
# Shortcut for downloading HF models
#
# Usage:
# ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
# ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
# ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
#
# all logs go to stderr
function log {
echo "$@" 1>&2
}
function usage {
log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [-h|--help]"
exit 1
}
# check for curl or wget
function has_cmd {
if ! [ -x "$(command -v $1)" ]; then
return 1
fi
}
if has_cmd wget; then
cmd="wget -q --show-progress -c -O %s %s"
elif has_cmd curl; then
cmd="curl -C - -f -o %s -L %s"
else
log "[E] curl or wget not found"
exit 1
fi
url=""
repo=""
file=""
# parse args
while [[ $# -gt 0 ]]; do
case "$1" in
--url)
url="$2"
shift 2
;;
--repo)
repo="$2"
shift 2
;;
--file)
file="$2"
shift 2
;;
-h|--help)
usage
;;
*)
url="$1"
shift
;;
esac
done
if [ -n "$repo" ] && [ -n "$file" ]; then
url="https://huggingface.co/$repo/resolve/main/$file"
fi
if [ -z "$url" ]; then
log "[E] missing --url"
usage
fi
# check if the URL is a HuggingFace model, and if so, try to download it
is_url=false
if [[ ${#url} -gt 22 ]]; then
if [[ ${url:0:22} == "https://huggingface.co" ]]; then
is_url=true
fi
fi
if [ "$is_url" = false ]; then
log "[E] invalid URL, must start with https://huggingface.co"
exit 0
fi
# replace "blob/main" with "resolve/main"
url=${url/blob\/main/resolve\/main}
basename=$(basename $url)
log "[+] attempting to download $basename"
if [ -n "$cmd" ]; then
cmd=$(printf "$cmd" "$basename" "$url")
log "[+] $cmd"
if $cmd; then
echo $basename
exit 0
fi
fi
log "[-] failed to download"
exit 1

2
tests/test-autorelease.cpp
View file

@ -12,7 +12,7 @@ int main(int argc, char ** argv) {
auto * model_path = get_model_or_exit(argc, argv);
std::thread([&model_path]() {
llama_backend_init(false);
llama_backend_init();
auto * model = llama_load_model_from_file(model_path, llama_model_default_params());
auto * ctx = llama_new_context_with_model(model, llama_context_default_params());
llama_free(ctx);

5
tests/test-backend-ops.cpp
View file

@ -2129,14 +2129,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_pad());
test_cases.emplace_back(new test_leaky_relu());
// these tests are disabled to save execution time, but they can be handy for debugging
#if 0
#if !defined(__SANITIZE_THREAD__)
// FIXME: these tests use too much memory with thread sanitizer
test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
//test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
#endif
// these tests are disabled to save execution time, but they can be handy for debugging
#if 0
test_cases.emplace_back(new test_llama(1));
test_cases.emplace_back(new test_llama(2));
test_cases.emplace_back(new test_falcon(1));

2
tests/test-model-load-cancel.cpp
View file

@ -14,7 +14,7 @@ int main(int argc, char *argv[] ) {
fprintf(stderr, "using '%s'\n", model_path);
fclose(file);
llama_backend_init(false);
llama_backend_init();
auto params = llama_model_params{};
params.use_mmap = false;
params.progress_callback = [](float progress, void * ctx){

54
unicode.h
View file

@ -264,26 +264,29 @@ static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
offset += 1;
return result;
}
else if (!(utf8[offset + 0] & 0x40)) {
if (!(utf8[offset + 0] & 0x40)) {
throw std::invalid_argument("invalid character");
}
else if (!(utf8[offset + 0] & 0x20)) {
if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80))
if (!(utf8[offset + 0] & 0x20)) {
if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
offset += 2;
return result;
}
else if (!(utf8[offset + 0] & 0x10)) {
if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80))
if (!(utf8[offset + 0] & 0x10)) {
if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
offset += 3;
return result;
}
else if (!(utf8[offset + 0] & 0x08)) {
if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80))
if (!(utf8[offset + 0] & 0x08)) {
if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
offset += 4;
return result;
@ -331,21 +334,22 @@ static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t
offset += 1;
return result;
}
else {
if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00))
if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
throw std::invalid_argument("invalid character");
}
auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
offset += 2;
return result;
}
throw std::invalid_argument("invalid string");
}
static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
std::vector<uint32_t> result;
size_t offset = 0;
while (offset < utf16.size())
while (offset < utf16.size()) {
result.push_back(codepoint_from_utf16(utf16, offset));
}
return result;
}
@ -361,44 +365,52 @@ static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> &
static std::unordered_map<uint32_t, int> codepoint_type_map() {
std::unordered_map<uint32_t, int> codepoint_types;
for (auto p : digit_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
}
}
for (auto p : letter_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_LETTER;
}
}
for (auto p : whitespace_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
}
}
for (auto p : accent_mark_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
}
}
for (auto p : punctuation_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
}
}
for (auto p : symbol_ranges) {
for (auto i = p.first; i <= p.second; ++i)
for (auto i = p.first; i <= p.second; ++i) {
codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
}
}
for (auto p : control_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
}
}
return codepoint_types;
}
static int codepoint_type(uint32_t cp) {
static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
return codepoint_types[cp];
return codepoint_types.find(cp) == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : codepoint_types.at(cp);
}
static int codepoint_type(const std::string & utf8) {
if (utf8.length() == 0)
if (utf8.length() == 0) {
return CODEPOINT_TYPE_UNIDENTIFIED;
}
size_t offset = 0;
return codepoint_type(codepoint_from_utf8(utf8, offset));
}