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integrated gpt2 support

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This commit is contained in:
Concedo 2023-04-04 23:15:47 +08:00
parent 52de932842
commit 14273fea7a
9 changed files with 926 additions and 30 deletions
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16
Makefile
View file

@ -119,7 +119,7 @@ endif
BLAS_BUILD =
ifeq ($(OS),Windows_NT)
BLAS_BUILD = $(CXX) $(CXXFLAGS) ggml_blas.o ggml_v1.o expose.o common.o llama_adapter.o gptj_adapter.o libopenblas.lib -shared -o koboldcpp_blas.dll $(LDFLAGS)
BLAS_BUILD = $(CXX) $(CXXFLAGS) ggml_blas.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o libopenblas.lib -shared -o koboldcpp_blas.dll $(LDFLAGS)
else
BLAS_BUILD = @echo 'Your OS is $(OS) and does not appear to be Windows. If you want to use openblas, please link it manually with LLAMA_OPENBLAS=1'
endif
@ -166,8 +166,8 @@ expose.o: expose.cpp expose.h
llama_adapter.o:
$(CXX) $(CXXFLAGS) -c llama_adapter.cpp -o llama_adapter.o
gptj_adapter.o:
$(CXX) $(CXXFLAGS) -c gptj_adapter.cpp -o gptj_adapter.o
gpttype_adapter.o:
$(CXX) $(CXXFLAGS) -c gpttype_adapter.cpp -o gpttype_adapter.o
clean:
rm -vf *.o main quantize perplexity embedding main.exe quantize.exe koboldcpp.dll koboldcpp_blas.dll gptj.exe
@ -178,10 +178,10 @@ main: examples/main/main.cpp ggml.o llama.o common.o
@echo '==== Run ./main -h for help. ===='
@echo
llamalib: ggml.o ggml_v1.o expose.o common.o llama_adapter.o gptj_adapter.o
$(CXX) $(CXXFLAGS) ggml.o ggml_v1.o expose.o common.o llama_adapter.o gptj_adapter.o -shared -o koboldcpp.dll $(LDFLAGS)
llamalib: ggml.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o
$(CXX) $(CXXFLAGS) ggml.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o -shared -o koboldcpp.dll $(LDFLAGS)
llamalib_blas: ggml_blas.o ggml_v1.o expose.o common.o llama_adapter.o gptj_adapter.o
llamalib_blas: ggml_blas.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o
$(BLAS_BUILD)
quantize: examples/quantize/quantize.cpp ggml.o llama.o
@ -193,8 +193,8 @@ perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o common.o
embedding: examples/embedding/embedding.cpp ggml.o llama.o common.o
$(CXX) $(CXXFLAGS) examples/embedding/embedding.cpp ggml.o llama.o common.o -o embedding $(LDFLAGS)
gptj: ggml_v1.o
$(CXX) $(CXXFLAGS) otherarch/gptj_v1_main.cpp otherarch/utils.cpp ggml_v1.o -o gptj $(LDFLAGS)
gpt2: ggml_v1.o
$(CXX) $(CXXFLAGS) otherarch/gpt2_v1.cpp otherarch/utils.cpp ggml_v1.o -o gpt2 $(LDFLAGS)
#
# Tests
#

25
expose.cpp
View file

@ -34,18 +34,18 @@ extern "C"
if(file_format==FileFormat::GPTJ1 || file_format==FileFormat::GPTJ2 || file_format==FileFormat::GPTJ3)
{
printf("\n---\nIdentified as GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
ModelLoadResult lr = gptj_load_model(inputs, file_format);
ModelLoadResult lr = gpttype_load_model(inputs, file_format);
if (lr == ModelLoadResult::RETRY_LOAD)
{
file_format = FileFormat::GPTJ2;
printf("\n---\nRetrying as GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
lr = gptj_load_model(inputs, file_format);
lr = gpttype_load_model(inputs, file_format);
}
if (lr == ModelLoadResult::RETRY_LOAD)
{
file_format = FileFormat::GPTJ3;
printf("\n---\nRetrying as GPT-J model: (ver %d)\nAttempting to Load...\n---\n", file_format);
lr = gptj_load_model(inputs, file_format);
lr = gpttype_load_model(inputs, file_format);
}
if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
@ -57,6 +57,19 @@ extern "C"
return true;
}
}
else if(file_format==FileFormat::GPT2)
{
printf("\n---\nIdentified as GPT-2 model: (ver %d)\nAttempting to Load...\n---\n", file_format);
ModelLoadResult lr = gpttype_load_model(inputs, file_format);
if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
{
return false;
}
else
{
return true;
}
}
else
{
printf("\n---\nIdentified as LLAMA model: (ver %d)\nAttempting to Load...\n---\n", file_format);
@ -66,13 +79,13 @@ extern "C"
generation_outputs generate(const generation_inputs inputs, generation_outputs &output)
{
if (file_format == FileFormat::GPTJ1 || file_format == FileFormat::GPTJ2 || file_format==FileFormat::GPTJ3)
if (file_format == FileFormat::GPTJ1 || file_format == FileFormat::GPTJ2 || file_format==FileFormat::GPTJ3 || file_format==FileFormat::GPT2)
{
return gptj_generate(inputs, output);
return gpttype_generate(inputs, output);
}
else
{
return llama_generate(inputs, output);
}
}
}
}

77
gptj_adapter.cpp → gpttype_adapter.cpp
View file

@ -15,12 +15,14 @@
#include "otherarch/utils.cpp"
#include "otherarch/gptj_v1.cpp"
#include "otherarch/gptj_v2.cpp"
#include "otherarch/gpt2_v1.cpp"
//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
static FileFormat file_format = FileFormat::BADFORMAT;
static gpt_vocab vocab;
static gptj_model_v1 model_v1;
static gptj_model model_v2;
static gpt2_model model_gpt2;
static gpt_params params;
static int n_past = 0;
static int n_threads = 4;
@ -31,7 +33,7 @@ static std::vector<gpt_vocab::id> current_context_tokens;
static size_t mem_per_token = 0;
static std::vector<float> logits;
ModelLoadResult gptj_load_model(const load_model_inputs inputs, FileFormat in_file_format)
ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format)
{
ggml_time_init();
@ -40,8 +42,20 @@ ModelLoadResult gptj_load_model(const load_model_inputs inputs, FileFormat in_fi
n_batch = params.n_batch = inputs.batch_size;
modelname = params.model = inputs.model_filename;
if (file_format == FileFormat::GPTJ1 || file_format == FileFormat::GPTJ2)
{
if (file_format == FileFormat::GPT2)
{
ModelLoadResult res = gpt2_model_load(params.model, model_gpt2, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
// determine the required inference memory per token:
gpt2_eval(model_gpt2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
return ModelLoadResult::SUCCESS;
}
else if (file_format == FileFormat::GPTJ1 || file_format == FileFormat::GPTJ2)
{
ModelLoadResult res = legacy_gptj_model_load(params.model, model_v1, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
@ -80,7 +94,7 @@ ModelLoadResult gptj_load_model(const load_model_inputs inputs, FileFormat in_fi
generation_outputs gptj_generate(const generation_inputs inputs, generation_outputs &output)
generation_outputs gpttype_generate(const generation_inputs inputs, generation_outputs &output)
{
params.prompt = inputs.prompt;
params.seed = inputs.seed;
@ -110,7 +124,20 @@ generation_outputs gptj_generate(const generation_inputs inputs, generation_outp
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
//truncate to front of the prompt if its too long
auto nctx = ( (file_format == FileFormat::GPTJ1||file_format == FileFormat::GPTJ2)? model_v1.hparams.n_ctx:model_v2.hparams.n_ctx);
int32_t nctx = 512;
if(file_format == FileFormat::GPTJ1||file_format == FileFormat::GPTJ2)
{
nctx = model_v1.hparams.n_ctx;
}
else if(file_format==FileFormat::GPTJ3)
{
nctx = model_v2.hparams.n_ctx;
}
else if(file_format==FileFormat::GPT2)
{
nctx = model_gpt2.hparams.n_ctx;
}
if (embd_inp.size() + params.n_predict > nctx)
{
int offset = embd_inp.size() - nctx + params.n_predict;
@ -170,10 +197,25 @@ generation_outputs gptj_generate(const generation_inputs inputs, generation_outp
timer_start();
double time1 = 0, time2 = 0;
unsigned int embd_inp_size = embd_inp.size();
const int n_vocab = ((file_format == FileFormat::GPTJ1||file_format == FileFormat::GPTJ2)? model_v1.hparams.n_vocab:model_v2.hparams.n_vocab);
int32_t n_vocab = 0;
if(file_format == FileFormat::GPTJ1||file_format == FileFormat::GPTJ2)
{
n_vocab = model_v1.hparams.n_vocab;
}
else if(file_format == FileFormat::GPTJ3)
{
n_vocab = model_v2.hparams.n_vocab;
}
else if(file_format == FileFormat::GPT2)
{
n_vocab = model_gpt2.hparams.n_vocab;
}
else
{
printf("Bad format!");
}
printf("\n");
while (remaining_tokens > 0)
{
gpt_vocab::id id = 0;
@ -192,10 +234,17 @@ generation_outputs gptj_generate(const generation_inputs inputs, generation_outp
}
bool evalres = false;
if(file_format==FileFormat::GPTJ1 || file_format==FileFormat::GPTJ2)
//print_tok_vec(logits);
if(file_format==FileFormat::GPT2)
{
evalres = gpt2_eval(model_gpt2, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else if(file_format==FileFormat::GPTJ1 || file_format==FileFormat::GPTJ2)
{
evalres = legacy_gptj_eval(model_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}else
}
else
{
evalres = gptj_eval(model_v2, params.n_threads, n_past, embd, logits, mem_per_token);
}
@ -229,11 +278,13 @@ generation_outputs gptj_generate(const generation_inputs inputs, generation_outp
}
{
// set the logit of the eos token (2) to zero to avoid sampling it
logits[50256] = 0;
//set logits of opening square bracket to zero.
// set the logit of the eos token (2) to zero to avoid sampling it
logits[50256] = (logits[50256]<0?logits[50256]:0);
//gpt2 uses negative logits, so we cant zero it
id = gptj_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab),last_n_tokens,repeat_penalty, top_k, top_p, temp, rng);
id = gptj_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);

BIN
koboldcpp.dll
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BIN
koboldcpp_blas.dll
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26
model_adapter.cpp
View file

@ -40,7 +40,27 @@ void print_tok_vec(std::vector<int> &embd)
first = false;
std::cout << i;
}
std::cout << "]";
std::cout << "]\n";
}
void print_tok_vec(std::vector<float> &embd)
{
std::cout << "[";
bool first = true;
int n = 0;
for (auto i : embd)
{
if (!first)
{
std::cout << ',';
}
first = false;
std::cout << i;
if(++n>20)
{
break;
}
}
std::cout << "]\n";
}
//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
@ -67,6 +87,10 @@ void print_tok_vec(std::vector<int> &embd)
{
fileformat = FileFormat::GPTJ1;
}
if(vocabsiz==50257)
{
fileformat = FileFormat::GPT2;
}
}
else if(magic == 0x67676d66) //v2 format ggmf
{

5
model_adapter.h
View file

@ -36,11 +36,12 @@ enum ModelLoadResult
bool llama_load_model(const load_model_inputs inputs, FileFormat file_format);
generation_outputs llama_generate(const generation_inputs inputs, generation_outputs &output);
ModelLoadResult gptj_load_model(const load_model_inputs inputs, FileFormat in_file_format);
generation_outputs gptj_generate(const generation_inputs inputs, generation_outputs &output);
ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format);
generation_outputs gpttype_generate(const generation_inputs inputs, generation_outputs &output);
void timer_start();
double timer_check();
void print_tok_vec(std::vector<int> &embd);
void print_tok_vec(std::vector<float> &embd);
FileFormat check_file_format(const std::string & fname);

753
otherarch/gpt2_v1.cpp Normal file
View file

@ -0,0 +1,753 @@
#include "ggml_v1.h"
#include "otherarch.h"
#include "utils.h"
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <iostream>
#include <unistd.h>
// load the model's weights from a file
ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, FileFormat file_format) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return ModelLoadResult::FAIL;
}
// verify magic
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != 0x67676d6c) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return ModelLoadResult::FAIL;
}
}
// load hparams
{
auto & hparams = model.hparams;
fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
fin.read((char *) &hparams.f16, sizeof(hparams.f16));
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: f16 = %d\n", __func__, hparams.f16);
}
// load vocab
{
int32_t n_vocab = 0;
fin.read((char *) &n_vocab, sizeof(n_vocab));
if (n_vocab != model.hparams.n_vocab) {
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
return ModelLoadResult::FAIL;
}
std::string word;
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
word.resize(len);
fin.read((char *) word.data(), len);
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
}
}
// for the big tensors, we have the option to store the data in 16-bit floats
// in order to save memory and also to speed up the computation
const ggml_v1_type wtype = model.hparams.f16 ? GGML_V1_TYPE_F16 : GGML_V1_TYPE_F32;
auto & ctx = model.ctx;
size_t ctx_size = 0;
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
ctx_size += n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // ln_f_g
ctx_size += n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // ln_f_b
ctx_size += n_vocab*n_embd*ggml_v1_type_size(wtype); // wte
ctx_size += n_ctx*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // wpe
ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_1_g
ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_1_b
ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_2_g
ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_2_b
ctx_size += n_layer*(3*n_embd*n_embd*ggml_v1_type_size(wtype)); // c_attn_attn_w
ctx_size += n_layer*( 3*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_attn_attn_b
ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_size(wtype)); // c_attn_proj_w
ctx_size += n_layer*( n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_attn_proj_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_size(wtype)); // c_mlp_fc_w
ctx_size += n_layer*( 4*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_mlp_fc_b
ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_size(wtype)); // c_mlp_proj_w
ctx_size += n_layer*( n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_mlp_proj_b
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // memory_v
ctx_size += (6 + 12*n_layer)*256; // object overhead
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
{
struct ggml_v1_init_params params = {
.mem_size = ctx_size,
.mem_buffer = NULL,
};
model.ctx = ggml_v1_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_v1_init() failed\n", __func__);
return ModelLoadResult::FAIL;
}
}
// prepare memory for the weights
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
model.layers.resize(n_layer);
model.ln_f_g = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
model.ln_f_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
model.wte = ggml_v1_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.wpe = ggml_v1_new_tensor_2d(ctx, GGML_V1_TYPE_F32, n_embd, n_ctx);
// map by name
model.tensors["model/ln_f/g"] = model.ln_f_g;
model.tensors["model/ln_f/b"] = model.ln_f_b;
model.tensors["model/wte"] = model.wte;
model.tensors["model/wpe"] = model.wpe;
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
layer.ln_1_g = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
layer.ln_1_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
layer.ln_2_g = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
layer.ln_2_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
layer.c_attn_attn_w = ggml_v1_new_tensor_2d(ctx, wtype, 3*n_embd, n_embd);
layer.c_attn_attn_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, 3*n_embd);
layer.c_attn_proj_w = ggml_v1_new_tensor_2d(ctx, wtype, n_embd, n_embd);
layer.c_attn_proj_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
layer.c_mlp_fc_w = ggml_v1_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
layer.c_mlp_fc_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, 4*n_embd);
layer.c_mlp_proj_w_trans = ggml_v1_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
layer.c_mlp_proj_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
// map by name
model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w_trans;
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
}
}
// key + value memory
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
model.memory_v = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
const size_t memory_size = ggml_v1_nbytes(model.memory_k) + ggml_v1_nbytes(model.memory_v);
printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
// load weights
{
size_t total_size = 0;
while (true) {
int32_t n_dims;
int32_t length;
int32_t ftype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (fin.eof()) {
break;
}
int32_t nelements = 1;
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
nelements *= ne[i];
}
std::string name(length, 0);
fin.read(&name[0], length);
if (model.tensors.find(name.data()) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
return ModelLoadResult::FAIL;
}
auto tensor = model.tensors[name.data()];
if (ggml_v1_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
return ModelLoadResult::FAIL;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
return ModelLoadResult::FAIL;
}
const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_v1_fp16_t);
if (nelements*bpe != ggml_v1_nbytes(tensor)) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_v1_nbytes(tensor), nelements*bpe);
return ModelLoadResult::FAIL;
}
fin.read(reinterpret_cast<char *>(tensor->data), ggml_v1_nbytes(tensor));
//printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_v1_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_v1_nbytes(tensor);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
}
fin.close();
return ModelLoadResult::SUCCESS;
}
// evaluate the transformer
//
// - model: the model
// - n_threads: number of threads to use
// - n_past: the context size so far
// - embd_inp: the embeddings of the tokens in the context
// - embd_w: the predicted logits for the next token
//
bool gpt2_eval(
const gpt2_model & model,
const int n_threads,
const int n_past,
const std::vector<gpt_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token,
FileFormat file_format) {
const int N = embd_inp.size();
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_vocab;
static size_t buf_size = 256u*1024*1024;
static void * buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
return false;
}
}
struct ggml_v1_init_params params = {
.mem_size = buf_size,
.mem_buffer = buf,
};
struct ggml_v1_context * ctx0 = ggml_v1_init(params);
struct ggml_v1_cgraph gf = { .n_threads = n_threads };
struct ggml_v1_tensor * embd = ggml_v1_new_tensor_1d(ctx0, GGML_V1_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_v1_element_size(embd));
struct ggml_v1_tensor * position = ggml_v1_new_tensor_1d(ctx0, GGML_V1_TYPE_I32, N);
for (int i = 0; i < N; ++i) {
((int32_t *) position->data)[i] = n_past + i;
}
// wte + wpe
struct ggml_v1_tensor * inpL =
ggml_v1_add(ctx0,
ggml_v1_get_rows(ctx0, model.wte, embd),
ggml_v1_get_rows(ctx0, model.wpe, position));
for (int il = 0; il < n_layer; ++il) {
struct ggml_v1_tensor * cur;
// norm
{
// [ 768, N]
cur = ggml_v1_norm(ctx0, inpL);
// cur = ln_1_g*cur + ln_1_b
// [ 768, N]
cur = ggml_v1_add(ctx0,
ggml_v1_mul(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].ln_1_g, cur),
cur),
ggml_v1_repeat(ctx0, model.layers[il].ln_1_b, cur));
}
// attn
// [2304, 768] - model.layers[il].c_attn_attn_w
// [2304, 1] - model.layers[il].c_attn_attn_b
// [ 768, N] - cur (in)
// [2304, N] - cur (out)
//
// cur = attn_w*cur + attn_b
// [2304, N]
{
cur = ggml_v1_mul_mat(ctx0,
ggml_v1_transpose(ctx0, model.layers[il].c_attn_attn_w),
cur);
cur = ggml_v1_add(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
cur);
}
// self-attention
{
struct ggml_v1_tensor * Qcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
struct ggml_v1_tensor * Kcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
struct ggml_v1_tensor * Vcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
// store key and value to memory
if (N >= 1) {
struct ggml_v1_tensor * k = ggml_v1_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v1_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
struct ggml_v1_tensor * v = ggml_v1_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_v1_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Kcur, k));
ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Vcur, v));
}
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
// [64, N, 12]
struct ggml_v1_tensor * Q =
ggml_v1_permute(ctx0,
ggml_v1_cpy(ctx0,
Qcur,
ggml_v1_new_tensor_3d(ctx0, GGML_V1_TYPE_F32, n_embd/n_head, n_head, N)),
0, 2, 1, 3);
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
// [64, n_past + N, 12]
struct ggml_v1_tensor * K =
ggml_v1_permute(ctx0,
ggml_v1_reshape_3d(ctx0,
ggml_v1_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// GG: flash attention
//struct ggml_v1_tensor * V =
// ggml_v1_cpy(ctx0,
// ggml_v1_permute(ctx0,
// ggml_v1_reshape_3d(ctx0,
// ggml_v1_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_v)*n_embd),
// n_embd/n_head, n_head, n_past + N),
// 1, 2, 0, 3),
// ggml_v1_new_tensor_3d(ctx0, GGML_V1_TYPE_F32, n_past + N, n_embd/n_head, n_head));
//struct ggml_v1_tensor * KQV = ggml_v1_flash_attn(ctx0, Q, K, V, true);
// K * Q
// [n_past + N, N, 12]
struct ggml_v1_tensor * KQ = ggml_v1_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// [n_past + N, N, 12]
struct ggml_v1_tensor * KQ_scaled =
ggml_v1_scale(ctx0,
KQ,
ggml_v1_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// KQ_masked = mask_past(KQ_scaled)
// [n_past + N, N, 12]
struct ggml_v1_tensor * KQ_masked = ggml_v1_diag_mask_inf(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// [n_past + N, N, 12]
struct ggml_v1_tensor * KQ_soft_max = ggml_v1_soft_max(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
// [n_past + N, 64, 12]
struct ggml_v1_tensor * V_trans =
ggml_v1_permute(ctx0,
ggml_v1_reshape_3d(ctx0,
ggml_v1_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_v)*n_embd),
n_embd/n_head, n_head, n_past + N),
1, 2, 0, 3);
// KQV = transpose(V) * KQ_soft_max
// [64, N, 12]
struct ggml_v1_tensor * KQV = ggml_v1_mul_mat(ctx0, V_trans, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
// [64, 12, N]
struct ggml_v1_tensor * KQV_merged = ggml_v1_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_embd, N)
// [768, N]
cur = ggml_v1_cpy(ctx0,
KQV_merged,
ggml_v1_new_tensor_2d(ctx0, GGML_V1_TYPE_F32, n_embd, N));
}
// projection
// [ 768, 768] - model.layers[il].c_attn_proj_w
// [ 768, 1] - model.layers[il].c_attn_proj_b
// [ 768, N] - cur (in)
// [ 768, N] - cur (out)
//
// cur = proj_w*cur + proj_b
// [768, N]
{
cur = ggml_v1_mul_mat(ctx0,
ggml_v1_transpose(ctx0, model.layers[il].c_attn_proj_w),
cur);
cur = ggml_v1_add(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
cur);
}
// add the input
cur = ggml_v1_add(ctx0, cur, inpL);
struct ggml_v1_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_v1_norm(ctx0, inpFF);
// cur = ln_2_g*cur + ln_2_b
// [ 768, N]
cur = ggml_v1_add(ctx0,
ggml_v1_mul(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].ln_2_g, cur),
cur),
ggml_v1_repeat(ctx0, model.layers[il].ln_2_b, cur));
}
// fully connected
// [3072, 768] - model.layers[il].c_mlp_fc_w
// [3072, 1] - model.layers[il].c_mlp_fc_b
// [ 768, N] - cur (in)
// [3072, N] - cur (out)
//
// cur = fc_w*cur + fc_b
// [3072, N]
cur = ggml_v1_mul_mat(ctx0,
ggml_v1_transpose(ctx0, model.layers[il].c_mlp_fc_w),
cur);
cur = ggml_v1_add(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
cur);
// GELU activation
// [3072, N]
cur = ggml_v1_gelu(ctx0, cur);
// projection
// [ 768, 3072] - model.layers[il].c_mlp_proj_w
// [ 768, 1] - model.layers[il].c_mlp_proj_b
// [3072, N] - cur (in)
// [ 768, N] - cur (out)
//
// cur = proj_w*cur + proj_b
// [768, N]
cur = ggml_v1_mul_mat(ctx0,
model.layers[il].c_mlp_proj_w_trans,
cur);
cur = ggml_v1_add(ctx0,
ggml_v1_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
cur);
}
// input for next layer
inpL = ggml_v1_add(ctx0, cur, inpFF);
}
// norm
{
// [ 768, N]
inpL = ggml_v1_norm(ctx0, inpL);
// inpL = ln_f_g*inpL + ln_f_b
// [ 768, N]
inpL = ggml_v1_add(ctx0,
ggml_v1_mul(ctx0,
ggml_v1_repeat(ctx0, model.ln_f_g, inpL),
inpL),
ggml_v1_repeat(ctx0, model.ln_f_b, inpL));
}
// inpL = WTE * inpL
// [ 768, 50257] - model.wte
// [ 768, N] - inpL
inpL = ggml_v1_mul_mat(ctx0, model.wte, inpL);
// logits -> probs
//inpL = ggml_v1_soft_max(ctx0, inpL);
// run the computation
ggml_v1_build_forward_expand(&gf, inpL);
ggml_v1_graph_compute (ctx0, &gf);
//if (n_past%100 == 0) {
// ggml_v1_graph_print (&gf);
// ggml_v1_graph_dump_dot(&gf, NULL, "gpt-2.dot");
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_v1_get_data(inpL), sizeof(float)*n_vocab*N);
// return result just for the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_v1_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
if (mem_per_token == 0) {
mem_per_token = ggml_v1_used_mem(ctx0)/N;
}
//printf("used_mem = %zu\n", ggml_v1_used_mem(ctx0));
ggml_v1_free(ctx0);
return true;
}
// int main(int argc, char ** argv) {
// ggml_v1_time_init();
// const int64_t t_main_start_us = ggml_v1_time_us();
// gpt_params params;
// params.model = "models/gpt-2-117M/ggml-model.bin";
// if (utils_gpt_params_parse(argc, argv, params) == false) {
// return 1;
// }
// if (params.seed < 0) {
// params.seed = time(NULL);
// }
// printf("%s: seed = %d\n", __func__, params.seed);
// std::mt19937 rng(params.seed);
// if (params.prompt.empty()) {
// if( !isatty(STDIN_FILENO) ){
// std::string line;
// while( std::getline(std::cin, line) ){
// params.prompt = params.prompt + "\n" + line;
// }
// } else {
// params.prompt = utils_gpt_random_prompt(rng);
// }
// }
// int64_t t_load_us = 0;
// gpt_vocab vocab;
// gpt2_model model;
// // load the model
// {
// const int64_t t_start_us = ggml_v1_time_us();
// if (!gpt2_model_load(params.model, model, vocab, FileFormat::GPT2)) {
// fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
// return 1;
// }
// t_load_us = ggml_v1_time_us() - t_start_us;
// }
// int n_past = 0;
// int64_t t_sample_us = 0;
// int64_t t_predict_us = 0;
// std::vector<float> logits;
// // tokenize the prompt
// std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
// params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
// printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
// printf("\n");
// // submit the input prompt token-by-token
// // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
// std::vector<gpt_vocab::id> embd;
// // determine the required inference memory per token:
// size_t mem_per_token = 0;
// gpt2_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, FileFormat::GPT2);
// for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
// // predict
// if (embd.size() > 0) {
// const int64_t t_start_us = ggml_v1_time_us();
// if (!gpt2_eval(model, params.n_threads, n_past, embd, logits, mem_per_token, FileFormat::GPT2)) {
// printf("Failed to predict\n");
// return 1;
// }
// t_predict_us += ggml_v1_time_us() - t_start_us;
// }
// n_past += embd.size();
// embd.clear();
// if (i >= embd_inp.size()) {
// // sample next token
// const int top_k = params.top_k;
// const float top_p = params.top_p;
// const float temp = params.temp;
// const int n_vocab = model.hparams.n_vocab;
// gpt_vocab::id id = 0;
// {
// const int64_t t_start_sample_us = ggml_v1_time_us();
// id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
// t_sample_us += ggml_v1_time_us() - t_start_sample_us;
// }
// // add it to the context
// embd.push_back(id);
// } else {
// // if here, it means we are still processing the input prompt
// for (int k = i; k < embd_inp.size(); k++) {
// embd.push_back(embd_inp[k]);
// if (embd.size() >= params.n_batch) {
// break;
// }
// }
// i += embd.size() - 1;
// }
// // display text
// for (auto id : embd) {
// printf("%s", vocab.id_to_token[id].c_str());
// }
// fflush(stdout);
// // end of text token
// if (embd.back() == 50256) {
// break;
// }
// }
// // report timing
// {
// const int64_t t_main_end_us = ggml_v1_time_us();
// printf("\n\n");
// printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
// printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
// printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
// printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
// printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
// }
// ggml_v1_free(model.ctx);
// return 0;
// }

54
otherarch/otherarch.h
View file

@ -113,6 +113,60 @@ struct gptj_model {
std::map<std::string, struct ggml_tensor *> tensors;
};
// default hparams (GPT-2 117M)
struct gpt2_hparams {
int32_t n_vocab = 50257;
int32_t n_ctx = 1024;
int32_t n_embd = 768;
int32_t n_head = 12;
int32_t n_layer = 12;
int32_t f16 = 1;
};
struct gpt2_layer {
// normalization
struct ggml_v1_tensor * ln_1_g;
struct ggml_v1_tensor * ln_1_b;
struct ggml_v1_tensor * ln_2_g;
struct ggml_v1_tensor * ln_2_b;
// attention
struct ggml_v1_tensor * c_attn_attn_w;
struct ggml_v1_tensor * c_attn_attn_b;
struct ggml_v1_tensor * c_attn_proj_w;
struct ggml_v1_tensor * c_attn_proj_b;
// mlp
struct ggml_v1_tensor * c_mlp_fc_w;
struct ggml_v1_tensor * c_mlp_fc_b;
struct ggml_v1_tensor * c_mlp_proj_w_trans; // transposed for efficiency
struct ggml_v1_tensor * c_mlp_proj_b;
};
struct gpt2_model {
gpt2_hparams hparams;
// normalization
struct ggml_v1_tensor * ln_f_g;
struct ggml_v1_tensor * ln_f_b;
struct ggml_v1_tensor * wte; // position embedding
struct ggml_v1_tensor * wpe; // token embedding
std::vector<gpt2_layer> layers;
// key + value memory
struct ggml_v1_tensor * memory_k;
struct ggml_v1_tensor * memory_v;
//
struct ggml_v1_context * ctx;
std::map<std::string, struct ggml_v1_tensor *> tensors;
};
ModelLoadResult legacy_gptj_model_load(const std::string &fname, gptj_model_v1 &model, gpt_vocab &vocab, FileFormat file_format);
bool legacy_gptj_eval(const gptj_model_v1 &model, const int n_threads, const int n_past, const std::vector<gpt_vocab::id> &embd_inp, std::vector<float> &embd_w, size_t &mem_per_token, FileFormat file_format);
ModelLoadResult gptj_model_load(const std::string &fname, gptj_model &model, gpt_vocab &vocab);