vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	.gitignore
#	Makefile
#	ggml-opencl.cpp
#	llama.cpp
This commit is contained in:
Concedo 2023-06-29 17:35:28 +08:00
commit dff5575647
19 changed files with 923 additions and 244 deletions
Download patch file Download diff file Expand all files Collapse all files

3
.gitignore vendored
View file

@ -1,5 +1,6 @@
*.o
*.a
*.so
.DS_Store
.build/
.cache/
@ -36,6 +37,7 @@ out/
/vdot
/server
/Pipfile
/embd-input-test
/libllama.so
arm_neon.h
@ -65,3 +67,4 @@ koboldcpp_failsafe.dll
koboldcpp_openblas.dll
koboldcpp_openblas_noavx2.dll
koboldcpp_clblast.dll
koboldcpp_cublas.dll

6
convert-lora-to-ggml.py
View file

@ -113,6 +113,10 @@ with open(output_path, "wb") as fout:
write_file_header(fout, params)
for k, v in model.items():
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
continue
if k.endswith("lora_A.weight"):
if v.dtype != torch.float16 and v.dtype != torch.float32:
v = v.float()
@ -120,7 +124,7 @@ with open(output_path, "wb") as fout:
else:
v = v.float()
t = v.numpy()
t = v.detach().numpy()
tname = translate_tensor_name(k)
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)

1
examples/CMakeLists.txt
View file

@ -39,6 +39,7 @@ else()
add_subdirectory(baby-llama)
add_subdirectory(train-text-from-scratch)
add_subdirectory(simple)
add_subdirectory(embd-input)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()

7
examples/common.cpp
View file

@ -416,13 +416,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
exit(1);
}
#ifdef GGML_USE_CUBLAS
if (!params.lora_adapter.empty() && params.n_gpu_layers > 0) {
fprintf(stderr, "%s: error: the simultaneous use of LoRAs and GPU acceleration is not supported", __func__);
exit(1);
}
#endif // GGML_USE_CUBLAS
if (escape_prompt) {
process_escapes(params.prompt);
}

4
examples/embd-input/.gitignore vendored Normal file
View file

@ -0,0 +1,4 @@
PandaGPT
MiniGPT-4
*.pth

15
examples/embd-input/CMakeLists.txt Normal file
View file

@ -0,0 +1,15 @@
set(TARGET embdinput)
add_library(${TARGET} embd-input-lib.cpp embd-input.h)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()
set(TARGET embd-input-test)
add_executable(${TARGET} embd-input-test.cpp)
target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

63
examples/embd-input/README.md Normal file
View file

@ -0,0 +1,63 @@
### Examples for input embedding directly
## Requirement
build `libembdinput.so`
run the following comman in main dir (../../).
```
make
```
## [LLaVA](https://github.com/haotian-liu/LLaVA/) example (llava.py)
1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
2. Convert it to ggml format.
3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin).
```
import torch
bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
pth_path = "./examples/embd_input/llava_projection.pth"
dic = torch.load(bin_path)
used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
torch.save({k: dic[k] for k in used_key}, pth_path)
```
4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`.
## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py)
1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
The `adapter_config.json` is
```
{
"peft_type": "LORA",
"fan_in_fan_out": false,
"bias": null,
"modules_to_save": null,
"r": 32,
"lora_alpha": 32,
"lora_dropout": 0.1,
"target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
}
```
2. Papare the `vicuna` v0 model.
3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
4. Clone the PandaGPT source.
```
git clone https://github.com/yxuansu/PandaGPT
```
5. Install the requirement of PandaGPT.
6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py)
1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`.
2. Clone the MiniGPT-4 source.
```
git clone https://github.com/Vision-CAIR/MiniGPT-4/
```
3. Install the requirement of PandaGPT.
4. Papare the `vicuna` v0 model.
5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`.

220
examples/embd-input/embd-input-lib.cpp Normal file
View file

@ -0,0 +1,220 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "embd-input.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
static llama_context ** g_ctx;
extern "C" {
struct MyModel* create_mymodel(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return nullptr;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return nullptr;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
struct MyModel * ret = new MyModel();
ret->ctx = ctx;
ret->params = params;
ret->n_past = 0;
// printf("ctx: %d\n", ret->ctx);
return ret;
}
void free_mymodel(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
llama_print_timings(ctx);
llama_free(ctx);
delete mymodel;
}
bool eval_float(void * model, float * input, int N){
MyModel * mymodel = (MyModel*)model;
llama_context * ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_emb = llama_n_embd(ctx);
int n_past = mymodel->n_past;
int n_batch = N; // params.n_batch;
for (int i = 0; i < (int) N; i += n_batch) {
int n_eval = (int) N - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_tokens(void * model, std::vector<llama_token> tokens) {
MyModel * mymodel = (MyModel* )model;
llama_context * ctx;
ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_past = mymodel->n_past;
for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_id(struct MyModel* mymodel, int id) {
std::vector<llama_token> tokens;
tokens.push_back(id);
return eval_tokens(mymodel, tokens);
}
bool eval_string(struct MyModel * mymodel,const char* str){
llama_context * ctx = mymodel->ctx;
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
eval_tokens(mymodel, embd_inp);
return true;
}
llama_token sampling_id(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
// int n_ctx = llama_n_ctx(ctx);
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
// const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
// const float repeat_penalty = params.repeat_penalty;
// const float alpha_presence = params.presence_penalty;
// const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
// const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// TODO: Apply penalties
// float nl_logit = logits[llama_token_nl()];
// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
// llama_sample_repetition_penalty(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, repeat_penalty);
// llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
// if (!penalize_nl) {
// logits[llama_token_nl()] = nl_logit;
// }
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
}
return id;
}
const char * sampling(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
int id = sampling_id(mymodel);
std::string ret;
if (id == llama_token_eos()) ret = "</s>";
else ret = llama_token_to_str(ctx, id);
eval_id(mymodel, id);
return ret.c_str();
}
}

35
examples/embd-input/embd-input-test.cpp Normal file
View file

@ -0,0 +1,35 @@
#include "embd-input.h"
#include <stdlib.h>
#include <random>
#include <string.h>
int main(int argc, char** argv) {
auto mymodel = create_mymodel(argc, argv);
int N = 10;
int max_tgt_len = 500;
int n_embd = llama_n_embd(mymodel->ctx);
// add random float embd to test evaluation
float * data = new float[N*n_embd];
std::default_random_engine e;
std::uniform_real_distribution<float> u(0,1);
for (int i=0;i<N*n_embd;i++) {
data[i] = u(e);
}
eval_string(mymodel, "user: what is the color of the flag of UN?");
eval_float(mymodel, data, N);
eval_string(mymodel, "assistant:");
eval_string(mymodel, mymodel->params.prompt.c_str());
const char* tmp;
for (int i=0; i<max_tgt_len; i++) {
tmp = sampling(mymodel);
if (strcmp(tmp, "</s>")==0) break;
printf("%s", tmp);
fflush(stdout);
}
printf("\n");
free_mymodel(mymodel);
return 0;
}

30
examples/embd-input/embd-input.h Normal file
View file

@ -0,0 +1,30 @@
#ifndef _EMBD_INPUT_H_
#define _EMBD_INPUT_H_ 1
#include "common.h"
#include "llama.h"
#include "build-info.h"
extern "C" {
typedef struct MyModel {
llama_context* ctx;
gpt_params params;
int n_past = 0;
} MyModel;
struct MyModel* create_mymodel(int argc, char ** argv);
bool eval_float(void* model, float* input, int N);
bool eval_tokens(void* model, std::vector<llama_token> tokens);
bool eval_id(struct MyModel* mymodel, int id);
bool eval_string(struct MyModel* mymodel, const char* str);
const char* sampling(struct MyModel* mymodel);
llama_token sampling_id(struct MyModel* mymodel);
void free_mymodel(struct MyModel* mymodel);
}
#endif

71
examples/embd-input/embd_input.py Normal file
View file

@ -0,0 +1,71 @@
import ctypes
from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
import numpy as np
import os
libc = cdll.LoadLibrary("./libembdinput.so")
libc.sampling.restype=c_char_p
libc.create_mymodel.restype=c_void_p
libc.eval_string.argtypes=[c_void_p, c_char_p]
libc.sampling.argtypes=[c_void_p]
libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int]
class MyModel:
def __init__(self, args):
argc = len(args)
c_str = [c_char_p(i.encode()) for i in args]
args_c = (c_char_p * argc)(*c_str)
self.model = c_void_p(libc.create_mymodel(argc, args_c))
self.max_tgt_len = 512
self.print_string_eval = True
def __del__(self):
libc.free_mymodel(self.model)
def eval_float(self, x):
libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1])
def eval_string(self, x):
libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
if self.print_string_eval:
print(x)
def eval_token(self, x):
libc.eval_id(self.model, x)
def sampling(self):
s = libc.sampling(self.model)
return s
def stream_generate(self, end="</s>"):
ret = b""
end = end.encode()
for _ in range(self.max_tgt_len):
tmp = self.sampling()
ret += tmp
yield tmp
if ret.endswith(end):
break
def generate_with_print(self, end="</s>"):
ret = b""
for i in self.stream_generate(end=end):
ret += i
print(i.decode(errors="replace"), end="", flush=True)
print("")
return ret.decode(errors="replace")
def generate(self, end="</s>"):
text = b"".join(self.stream_generate(end=end))
return text.decode(errors="replace")
if __name__ == "__main__":
model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
model.eval_string("""user: what is the color of the flag of UN?""")
x = np.random.random((5120,10))# , dtype=np.float32)
model.eval_float(x)
model.eval_string("""assistant:""")
for i in model.generate():
print(i.decode(errors="replace"), end="", flush=True)

70
examples/embd-input/llava.py Normal file
View file

@ -0,0 +1,70 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
from transformers import CLIPVisionModel, CLIPImageProcessor
from PIL import Image
# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
vision_tower = "openai/clip-vit-large-patch14"
select_hidden_state_layer = -2
# (vision_config.image_size // vision_config.patch_size) ** 2
image_token_len = (224//14)**2
class Llava:
def __init__(self, args):
self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
self.mm_projector = nn.Linear(1024, 5120)
self.model = MyModel(["main", *args])
def load_projection(self, path):
state = torch.load(path)
self.mm_projector.load_state_dict({
"weight": state["model.mm_projector.weight"],
"bias": state["model.mm_projector.bias"]})
def chat(self, question):
self.model.eval_string("user: ")
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate_with_print()
def chat_with_image(self, image, question):
with torch.no_grad():
embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
image_feature = select_hidden_state[:, 1:]
embd_image = self.mm_projector(image_feature)
embd_image = embd_image.cpu().numpy()[0]
self.model.eval_string("user: ")
self.model.eval_token(32003-2) # im_start
self.model.eval_float(embd_image.T)
for i in range(image_token_len-embd_image.shape[0]):
self.model.eval_token(32003-3) # im_patch
self.model.eval_token(32003-1) # im_end
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate_with_print()
if __name__=="__main__":
# model form liuhaotian/LLaVA-13b-delta-v1-1
a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
# Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
# Also here can use pytorch_model-00003-of-00003.bin directly.
a.load_projection(os.path.join(
os.path.dirname(__file__) ,
"llava_projetion.pth"))
respose = a.chat_with_image(
Image.open("./media/llama1-logo.png").convert('RGB'),
"what is the text in the picture?")
respose
a.chat("what is the color of it?")

128
examples/embd-input/minigpt4.py Normal file
View file

@ -0,0 +1,128 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
from PIL import Image
minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
sys.path.insert(0, minigpt4_path)
from minigpt4.models.blip2 import Blip2Base
from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
class MiniGPT4(Blip2Base):
"""
MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
"""
def __init__(self,
args,
vit_model="eva_clip_g",
q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp32",
freeze_vit=True,
freeze_qformer=True,
num_query_token=32,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=32,
end_sym='\n',
low_resource=False, # use 8 bit and put vit in cpu
device_8bit=0
):
super().__init__()
self.img_size = img_size
self.low_resource = low_resource
self.preprocessor = Blip2ImageEvalProcessor(img_size)
print('Loading VIT')
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
)
print('Loading VIT Done')
print('Loading Q-Former')
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features
)
self.Qformer.cls = None
self.Qformer.bert.embeddings.word_embeddings = None
self.Qformer.bert.embeddings.position_embeddings = None
for layer in self.Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.load_from_pretrained(url_or_filename=q_former_model)
print('Loading Q-Former Done')
self.llama_proj = nn.Linear(
self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.end_sym = end_sym
self.model = MyModel(["main", *args])
# system promt
self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you. Please answer my questions."
"###")
def encode_img(self, image):
image = self.preprocessor(image)
image = image.unsqueeze(0)
device = image.device
if self.low_resource:
self.vit_to_cpu()
image = image.to("cpu")
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_llama = self.llama_proj(query_output.last_hidden_state)
# atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
return inputs_llama
def load_projection(self, path):
state = torch.load(path)["model"]
self.llama_proj.load_state_dict({
"weight": state["llama_proj.weight"],
"bias": state["llama_proj.bias"]})
def chat(self, question):
self.model.eval_string("Human: ")
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
return self.model.generate_with_print(end="###")
def chat_with_image(self, image, question):
with torch.no_grad():
embd_image = self.encode_img(image)
embd_image = embd_image.cpu().numpy()[0]
self.model.eval_string("Human: <Img>")
self.model.eval_float(embd_image.T)
self.model.eval_string("</Img> ")
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
return self.model.generate_with_print(end="###")
if __name__=="__main__":
a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
a.load_projection(os.path.join(
os.path.dirname(__file__) ,
"pretrained_minigpt4.pth"))
respose = a.chat_with_image(
Image.open("./media/llama1-logo.png").convert('RGB'),
"what is the text in the picture?")
a.chat("what is the color of it?")

98
examples/embd-input/panda_gpt.py Normal file
View file

@ -0,0 +1,98 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
# use PandaGPT path
panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
imagebind_ckpt_path = "./models/panda_gpt/"
sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
from ImageBind.models import imagebind_model
from ImageBind import data
ModalityType = imagebind_model.ModalityType
max_tgt_len = 400
class PandaGPT:
def __init__(self, args):
self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
self.visual_encoder.eval()
self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
self.max_tgt_len = max_tgt_len
self.model = MyModel(["main", *args])
self.generated_text = ""
self.device = "cpu"
def load_projection(self, path):
state = torch.load(path, map_location="cpu")
self.llama_proj.load_state_dict({
"weight": state["llama_proj.weight"],
"bias": state["llama_proj.bias"]})
def eval_inputs(self, inputs):
self.model.eval_string("<Img>")
embds = self.extract_multimoal_feature(inputs)
for i in embds:
self.model.eval_float(i.T)
self.model.eval_string("</Img> ")
def chat(self, question):
return self.chat_with_image(None, question)
def chat_with_image(self, inputs, question):
if self.generated_text == "":
self.model.eval_string("###")
self.model.eval_string(" Human: ")
if inputs:
self.eval_inputs(inputs)
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
ret = self.model.generate_with_print(end="###")
self.generated_text += ret
return ret
def extract_multimoal_feature(self, inputs):
features = []
for key in ["image", "audio", "video", "thermal"]:
if key + "_paths" in inputs:
embeds = self.encode_data(key, inputs[key+"_paths"])
features.append(embeds)
return features
def encode_data(self, data_type, data_paths):
type_map = {
"image": ModalityType.VISION,
"audio": ModalityType.AUDIO,
"video": ModalityType.VISION,
"thermal": ModalityType.THERMAL,
}
load_map = {
"image": data.load_and_transform_vision_data,
"audio": data.load_and_transform_audio_data,
"video": data.load_and_transform_video_data,
"thermal": data.load_and_transform_thermal_data
}
load_function = load_map[data_type]
key = type_map[data_type]
inputs = {key: load_function(data_paths, self.device)}
with torch.no_grad():
embeddings = self.visual_encoder(inputs)
embeds = embeddings[key]
embeds = self.llama_proj(embeds).cpu().numpy()
return embeds
if __name__=="__main__":
a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
a.load_projection("./models/panda_gpt/adapter_model.bin")
a.chat_with_image(
{"image_paths": ["./media/llama1-logo.png"]},
"what is the text in the picture? 'llama' or 'lambda'?")
a.chat("what is the color of it?")

65
ggml-cuda.cu
View file

@ -223,6 +223,15 @@ static __global__ void add_f32(const float * x, const float * y, float * dst, co
dst[i] = x[i] + y[i];
}
static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = __hadd(x[i], __float2half(y[i]));
}
static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
@ -1235,7 +1244,7 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y,
}
static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nchannels_x) {
const half * x = (half *) vx;
const half * x = (const half *) vx;
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
@ -1283,9 +1292,9 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl
static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
const int row_stride_x, const int nchannels_x, const int channel_stride_x) {
const int row_stride_x, const int channel_stride_x) {
const half * x = (half *) vx;
const half * x = (const half *) vx;
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
@ -1328,14 +1337,14 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
}
static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
const float * xi = (float *) cxi;
const float * xi = (const float *) cxi;
float * dsti = (float *) cdsti;
*dsti = *xi;
}
static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
const float * xi = (float *) cxi;
const float * xi = (const float *) cxi;
half * dsti = (half *) cdsti;
*dsti = __float2half(*xi);
@ -1459,6 +1468,11 @@ static void add_f32_cuda(const float * x, const float * y, float * dst, const in
add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
}
static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
add_f16_f32_f16<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
}
static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
@ -1684,7 +1698,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda(
const dim3 block_nums(1, nrows_x, nchannels_x);
const dim3 block_dims(WARP_SIZE, 1, 1);
mul_mat_vec_nc_f16_f32<<<block_nums, block_dims, 0, stream>>>
(vx, y, dst, ncols_x, nrows_x, row_stride_x, nchannels_x, channel_stride_x);
(vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x);
}
static void ggml_cpy_f32_f32_cuda(
@ -1966,7 +1980,7 @@ inline void ggml_cuda_op_add(
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
cudaStream_t & cudaStream_main){
GGML_ASSERT(src0_ddf_i != nullptr);
GGML_ASSERT(src0_ddq_i != nullptr || src0_ddf_i != nullptr);
GGML_ASSERT(src1_ddf_i != nullptr);
GGML_ASSERT(dst_ddf_i != nullptr);
@ -1974,7 +1988,13 @@ inline void ggml_cuda_op_add(
const int64_t i01_diff = i01_high - i01_low;
// compute
add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne0*i01_diff, cudaStream_main);
} else {
GGML_ASSERT(false);
}
CUDA_CHECK(cudaGetLastError());
(void) src1;
@ -2572,8 +2592,14 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, true, true);
// ggml_cuda_add permits f16 dst even though this could in theory cause problems with the pointer arithmetic in ggml_cuda_op.
// Due to flatten_rows == true this does in practice not make a difference however.
// Better solution would be nice but right now that would require disproportionate changes.
GGML_ASSERT(
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
src1->type == GGML_TYPE_F32 &&
(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16));
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, false, true);
}
void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@ -2826,7 +2852,7 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) {
delete extra;
}
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) {
if (scratch && g_scratch_size == 0) {
return;
}
@ -2835,11 +2861,11 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
if (tensor->src0 != nullptr && tensor->src0->backend == GGML_BACKEND_CPU) {
const ggml_op src0_op = tensor->src0->op;
if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW) {
ggml_cuda_assign_buffers_impl(tensor->src0, scratch);
ggml_cuda_assign_buffers_impl(tensor->src0, scratch, force_inplace);
}
}
if (tensor->op == GGML_OP_CPY && tensor->src1->backend == GGML_BACKEND_CPU) {
ggml_cuda_assign_buffers_impl(tensor->src1, scratch);
ggml_cuda_assign_buffers_impl(tensor->src1, scratch, force_inplace);
}
tensor->backend = GGML_BACKEND_GPU;
@ -2847,11 +2873,12 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
memset(extra, 0, sizeof(*extra));
const bool inplace = (tensor->src0 != nullptr && tensor->src0->data == tensor->data) ||
tensor->op == GGML_OP_VIEW;
tensor->op == GGML_OP_VIEW ||
force_inplace;
const size_t size = ggml_nbytes(tensor);
CUDA_CHECK(cudaSetDevice(g_main_device));
if (inplace && tensor->src0->backend == GGML_BACKEND_GPU) {
if (inplace && (tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT)) {
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src0->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t offset = 0;
@ -2890,11 +2917,15 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
}
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, true);
ggml_cuda_assign_buffers_impl(tensor, true, false);
}
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false);
ggml_cuda_assign_buffers_impl(tensor, false, false);
}
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false, true);
}
void ggml_cuda_set_main_device(int main_device) {

1
ggml-cuda.h
View file

@ -29,6 +29,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
void ggml_cuda_free_data(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);

3
ggml.c
View file

@ -16684,7 +16684,8 @@ typedef pthread_t ggml_thread_t;
#endif
#ifdef __linux__
// Android's libc implementation "bionic" does not support setting affinity
#if defined(__linux__) && !defined(__BIONIC__)
void set_numa_thread_affinity(int thread_n, int n_threads) {
if (!ggml_is_numa()) {
return;

337
llama.cpp
View file

@ -364,96 +364,14 @@ static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml
return size / ggml_blck_size(type);
}
struct llama_load_tensor_shard {
std::vector<uint32_t> ne;
size_t size;
enum ggml_type type;
size_t file_idx;
size_t file_off;
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
enum llama_split_type {
SPLIT_NONE,
SPLIT_BY_COLUMNS,
SPLIT_BY_ROWS
};
struct llama_load_tensor {
std::vector<llama_load_tensor_shard> shards;
std::string name;
enum ggml_type type = GGML_TYPE_F32;
llama_split_type split_type = SPLIT_NONE;
std::vector<uint32_t> ne;
size_t file_off;
size_t size;
struct ggml_tensor * ggml_tensor = NULL;
uint8_t * data;
llama_load_tensor(const std::string & name) : name(name) {}
void calc_all() {
calc_type();
calc_split_type();
calc_ne();
calc_size();
}
void calc_type() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.type != first_shard.type) {
throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
}
}
type = first_shard.type;
}
void calc_split_type() {
if (shards.at(0).ne.size() == 1 || // 1D tensors are just duplicated in every file
shards.size() == 1) { // only one file?
split_type = SPLIT_NONE;
} else if (name.find("tok_embeddings.") == 0 ||
name.find(".attention.wo.weight") != std::string::npos ||
name.find(".feed_forward.w2.weight") != std::string::npos) {
split_type = SPLIT_BY_COLUMNS;
} else {
split_type = SPLIT_BY_ROWS;
}
}
void calc_ne() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.ne != first_shard.ne) {
throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
}
}
ne = first_shard.ne;
LLAMA_ASSERT(shards.size() <= UINT32_MAX);
uint32_t n_shards = (uint32_t) shards.size();
switch (split_type) {
case SPLIT_NONE:
ne = first_shard.ne;
break;
case SPLIT_BY_COLUMNS:
ne = {checked_mul<uint32_t>(first_shard.ne[0], n_shards),
first_shard.ne[1]};
break;
case SPLIT_BY_ROWS:
ne = {first_shard.ne[0],
checked_mul<uint32_t>(first_shard.ne[1], n_shards)};
break;
}
}
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
struct llama_load_tensors_map {
@ -476,13 +394,13 @@ struct llama_file_loader {
llama_hparams hparams;
llama_vocab vocab;
llama_file_loader(const char * fname, size_t file_idx, llama_load_tensors_map & tensors_map)
llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
read_magic();
read_hparams();
read_vocab();
read_tensor_metadata(file_idx, tensors_map);
read_tensor_metadata(tensors_map);
}
void read_magic() {
uint32_t magic = file.read_u32();
@ -539,19 +457,19 @@ struct llama_file_loader {
tok_score.score = score;
}
}
void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) {
void read_tensor_metadata(llama_load_tensors_map & tensors_map) {
while (file.tell() < file.size) {
llama_load_tensor_shard shard;
llama_load_tensor tensor;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
shard.type = (enum ggml_type) file.read_u32();
shard.ne.resize(n_dims);
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
tensor.type = (enum ggml_type) file.read_u32();
tensor.ne.resize(n_dims);
file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
}
switch (shard.type) {
switch (tensor.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
@ -566,30 +484,20 @@ struct llama_file_loader {
case GGML_TYPE_Q6_K:
break;
default: {
throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type));
}
}
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
// skip to the next multiple of 32 bytes
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
}
shard.file_idx = file_idx;
shard.file_off = file.tell();
// skip to the next multiple of 32 bytes
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
shard.calc_size();
file.seek(shard.size, SEEK_CUR);
tensor.file_off = file.tell();
tensor.name = name;
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
file.seek(tensor.size, SEEK_CUR);
auto it = tensors_map.name_to_idx.find(name);
size_t idx;
if (it != tensors_map.name_to_idx.end()) {
idx = it->second;
} else {
tensors_map.tensors.emplace_back(name);
idx = tensors_map.tensors.size() - 1;
tensors_map.name_to_idx.emplace(name, idx);
}
tensors_map.tensors.at(idx).shards.push_back(shard);
tensors_map.tensors.push_back(tensor);
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
}
}
};
@ -659,56 +567,19 @@ struct llama_file_saver {
};
struct llama_model_loader {
std::vector<std::unique_ptr<llama_file_loader>> file_loaders;
std::unique_ptr<llama_file_loader> file_loader;
llama_load_tensors_map tensors_map;
bool use_mmap;
size_t num_ggml_tensors_created = 0;
struct ggml_context * ggml_ctx = NULL;
std::unique_ptr<llama_mmap> mapping;
llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) {
auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
file_loaders.emplace_back(first_file);
uint32_t n_parts = vocab_only ? 1 : guess_n_parts();
for (uint32_t i = 1; i < n_parts; i++) {
std::string fname = fname_base + "." + std::to_string(i);
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
file_loaders.emplace_back(ith_file);
if (ith_file->hparams != first_file->hparams) {
throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
}
}
llama_model_loader(const std::string & fname_base, bool use_mmap) {
file_loader = std::unique_ptr<llama_file_loader>(new llama_file_loader(fname_base.c_str(), tensors_map));
if (!llama_mmap::SUPPORTED) {
use_mmap = false;
}
if (use_mmap && alignment_prevents_mmap()) {
fprintf(stderr, "llama.cpp: can't use mmap because tensors are not aligned; convert to new format to avoid this\n");
use_mmap = false;
}
this->use_mmap = use_mmap;
for (llama_load_tensor & lt : tensors_map.tensors) {
lt.calc_all();
}
}
bool alignment_prevents_mmap() {
for (const llama_load_tensor & lt : tensors_map.tensors) {
for (const llama_load_tensor_shard & shard : lt.shards) {
if (shard.file_off & 3) {
return true;
}
}
}
return false;
}
uint32_t guess_n_parts() const {
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
if (it == tensors_map.name_to_idx.end()) {
throw std::runtime_error(std::string("missing tok_embeddings.weight"));
}
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
}
void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
@ -774,7 +645,7 @@ struct llama_model_loader {
}
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size, ggml_is_numa()));
mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa()));
if (lmlock) {
lmlock->init(mapping->addr);
}
@ -830,45 +701,13 @@ struct llama_model_loader {
void load_data_for(llama_load_tensor & lt) {
if (use_mmap) {
LLAMA_ASSERT(lt.shards.size() == 1);
lt.data = (uint8_t *) mapping->addr + lt.shards.at(0).file_off;
} else if (lt.split_type == SPLIT_NONE) {
llama_file & file = file_loaders.at(lt.shards.at(0).file_idx)->file;
file.seek(lt.shards.at(0).file_off, SEEK_SET);
lt.data = (uint8_t *) mapping->addr + lt.file_off;
} else {
llama_file & file = file_loader->file;
file.seek(lt.file_off, SEEK_SET);
file.read_raw(lt.data, lt.size);
} else if (lt.split_type == SPLIT_BY_ROWS) {
size_t offset = 0;
for (llama_load_tensor_shard & shard : lt.shards) {
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
file.read_raw(lt.data + offset, shard.size);
offset += shard.size;
}
LLAMA_ASSERT(offset == lt.size);
} else if (lt.split_type == SPLIT_BY_COLUMNS) {
// Let's load the data into temporary buffers to ensure the OS performs large loads.
std::vector<llama_buffer> tmp_bufs(lt.shards.size());
for (size_t i = 0; i < lt.shards.size(); i++) {
llama_load_tensor_shard & shard = lt.shards.at(i);
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
tmp_bufs.at(i).resize(shard.size);
file.read_raw(tmp_bufs.at(i).addr, shard.size);
}
// Then reshape.
size_t num_rows = lt.ne.at(1);
size_t per_shard_row_size = lt.shards.at(0).size / num_rows;
size_t out_offset = 0;
for (size_t row = 0; row < num_rows; row++) {
for (llama_buffer & tmp_buf : tmp_bufs) {
memcpy(lt.data + out_offset,
tmp_buf.addr + row * per_shard_row_size,
per_shard_row_size);
out_offset += per_shard_row_size;
}
}
LLAMA_ASSERT(out_offset == lt.size);
}
if (0) {
print_checksum(lt);
}
@ -1067,12 +906,12 @@ static void llama_model_load_internal(
model.t_start_us = ggml_time_us();
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
vocab = std::move(ml->file_loaders.at(0)->vocab);
model.hparams = ml->file_loaders.at(0)->hparams;
vocab = std::move(ml->file_loader->vocab);
model.hparams = ml->file_loader->hparams;
model.n_gpu_layers = n_gpu_layers;
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
llama_file_version file_version = ml->file_loader->file_version;
auto & hparams = model.hparams;
{
@ -1106,7 +945,6 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size());
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
@ -1369,18 +1207,20 @@ static bool llama_model_load(
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
// - cgraph_fname: filename of the exported computation graph
// - lctx: llama context
// - tokens: new batch of tokens to process
// - embd embeddings input
// - n_tokens number of tokens
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads,
llama_context & lctx,
const llama_token * tokens,
const float * embd,
const int n_tokens,
const int n_past,
const int n_threads,
const char * cgraph_fname) {
// // enforce that the first token is BOS
@ -1424,12 +1264,18 @@ static bool llama_eval_internal(
ggml_cgraph gf = {};
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
struct ggml_tensor * cur;
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
struct ggml_tensor * inpL;
if (tokens) {
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
} else {
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start;
@ -2451,9 +2297,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype);
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype);
#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
@ -2654,6 +2499,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
}
//
// interface implementation
//
@ -2874,7 +2721,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
// create a name -> tensor map of the model to accelerate lookups
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (auto & kv: model.tensors_by_name) {
for (const auto & kv: model.tensors_by_name) {
model_tensors.insert(kv);
}
@ -2885,7 +2732,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
llama_buffer base_buf;
if (path_base_model) {
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
size_t ctx_size;
size_t mmapped_size;
@ -2903,7 +2750,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
// maybe this should in llama_model_loader
if (model_loader->use_mmap) {
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0, ggml_is_numa()));
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa()));
}
}
@ -2964,7 +2811,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
return false;
}
}
ggml_tensor* lora_tensor;
ggml_tensor * lora_tensor;
if (n_dims == 2) {
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
@ -2972,6 +2819,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
ggml_set_name(lora_tensor, "lora_tensor");
// load tensor data
size_t offset = fin.tellg();
@ -2987,6 +2835,21 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
ggml_tensor * dest_t = model_tensors[base_name];
offload_func_t offload_func = llama_nop;
offload_func_t offload_func_force_inplace = llama_nop;
#ifdef GGML_USE_CUBLAS
if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
if (dest_t->type != GGML_TYPE_F16) {
throw std::runtime_error(format(
"%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
}
offload_func = ggml_cuda_assign_buffers;
offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
}
#endif // GGML_USE_CUBLAS
ggml_tensor * base_t;
if (model_loader) {
// load from base model
@ -3014,7 +2877,12 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
}
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
GGML_ASSERT(loraA->type == GGML_TYPE_F32);
ggml_set_name(loraA, "loraA");
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
GGML_ASSERT(loraB->type == GGML_TYPE_F32);
ggml_set_name(loraB, "loraB");
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
@ -3024,19 +2892,32 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
ggml_set_name(scale_tensor, "scale_tensor");
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx, dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
r = ggml_add(lora_ctx, base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
r = ggml_cpy(lora_ctx, r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph gf = ggml_build_forward(r);
@ -3421,7 +3302,29 @@ int llama_eval(
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
// get a more accurate load time, upon first eval
// TODO: fix this
if (!ctx->has_evaluated_once) {
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
ctx->has_evaluated_once = true;
}
return 0;
}
int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
@ -3442,7 +3345,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}

8
llama.h
View file

@ -226,6 +226,14 @@ extern "C" {
int n_past,
int n_threads);
// Same as llama_eval, but use float matrix input directly.
LLAMA_API int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
int n_past,
int n_threads);
// Export a static computation graph for context of 511 and batch size of 1
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
// parameters here to keep things simple