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MPT: use upstream llama.cpp implementation (#1515)

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cebtenzzre 2023-10-19 15:25:17 -04:00 committed by GitHub
parent 0fe2e19691
commit 4338e72a51
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10 changed files with 24 additions and 1038 deletions
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4
gpt4all-backend/CMakeLists.txt
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@ -102,10 +102,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS)
gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
prepare_target(gptj llama-mainline)
add_library(mpt-${BUILD_VARIANT} SHARED
mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
prepare_target(mpt llama-mainline)
add_library(bert-${BUILD_VARIANT} SHARED
bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
target_compile_definitions(bert-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999)

2
gpt4all-backend/llama.cpp-mainline

@ -1 +1 @@
Subproject commit ffe96e1ebf9cdae1dc82b2049d9e45c1875472ab
Subproject commit a8ed8c858985ef94d97a3cf2c97085b680c6d5d0

2
gpt4all-backend/llamamodel.cpp
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@ -397,7 +397,7 @@ DLL_EXPORT bool magic_match(const char * fname) {
bool isValid = gguf_get_version(ctx_gguf) <= 2;
auto arch = get_arch_name(ctx_gguf);
isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon");
isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon" || arch == "mpt");
gguf_free(ctx_gguf);
return isValid;

969
gpt4all-backend/mpt.cpp
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@ -1,969 +0,0 @@
#define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#include "mpt_impl.h"
#include "utils.h"
#include "llmodel_shared.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <map>
#include <random>
#include <string>
#include <vector>
#include <iostream>
#if defined(_WIN32) && defined(_MSC_VER)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h>
#else
#include <unistd.h>
#endif
#include <sstream>
#include <thread>
#include <unordered_set>
#include <unordered_map>
#include <regex>
#include <ggml.h>
namespace {
const char *modelType_ = "MPT";
}
// default hparams (MPT 7B)
struct mpt_hparams {
int32_t n_vocab = 50432;
int32_t n_ctx = 2048;
int32_t n_embd = 4096;
int32_t n_head = 32;
int32_t n_layer = 32;
float alibi_bias_max = 8;
float clip_qkv = 0;
float norm_eps = 1e-5;
int32_t expand = 4;
};
struct mpt_layer {
// normalization
struct ggml_tensor * norm_1_w;
struct ggml_tensor * norm_2_w;
// attention
struct ggml_tensor * attn_Wqkv_w;
struct ggml_tensor * attn_out_proj_w;
// ff
struct ggml_tensor * ffn_up_proj_w;
struct ggml_tensor * ffn_down_proj_w;
};
struct mpt_model {
mpt_hparams hparams;
// normalization
struct ggml_tensor * norm_f_w;
struct ggml_tensor * wte; // position embedding
// mpt does weight tying
std::vector<mpt_layer> layers;
struct llm_kv_cache kv_self;
struct ggml_context * ctx;
llm_buffer eval_buf;
llm_buffer scr0_buf;
llm_buffer scr1_buf;
~mpt_model() {
if (ctx) {
ggml_free(ctx);
}
}
};
enum mpt_token_type {
MPT_TOKEN_TYPE_NORMAL = 1,
MPT_TOKEN_TYPE_CONTROL = 3,
};
using replit_piece_t = std::pair<std::size_t, float>;
using replit_piece_map_t = std::unordered_map<std::string, replit_piece_t>;
static const std::string replit_ws_symbol = "\342\226\201";
struct mpt_vocab {
bool is_replit = false;
gpt_vocab raw;
replit_piece_map_t piece_map;
std::vector<std::string> vocab;
const char * end_of_text() const {
return is_replit ? "<|endoftext|>" : "<|im_end|>";
}
};
std::pair<std::vector<LLModel::Token>, float> encode_word(const std::string & word, const replit_piece_map_t & model) {
std::vector<int> best_segmentations_starts(word.length() + 1, -1);
best_segmentations_starts[0] = 0;
std::vector<float> best_segmentations_scores(word.length() + 1, -std::numeric_limits<float>::infinity());
best_segmentations_scores[0] = 1.0;
for (size_t start_idx = 0; start_idx < word.length(); ++start_idx) {
float best_score_at_start = best_segmentations_scores[start_idx];
for (size_t end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) {
std::string token = word.substr(start_idx, end_idx - start_idx);
if (model.count(token) && best_score_at_start != -std::numeric_limits<float>::infinity()) {
float token_score = model.at(token).second;
float score = token_score + best_score_at_start;
if (best_segmentations_scores[end_idx] == -std::numeric_limits<float>::infinity() ||
best_segmentations_scores[end_idx] > score) {
best_segmentations_starts[end_idx] = start_idx;
best_segmentations_scores[end_idx] = score;
}
}
}
}
if (best_segmentations_scores.back() == -std::numeric_limits<float>::infinity()) {
return std::make_pair(std::vector<LLModel::Token>{0}, 0.0f);
}
float score = best_segmentations_scores.back();
int start = best_segmentations_starts.back();
int end = word.length();
std::vector<LLModel::Token> tokens;
while (start != 0) {
const auto token_id = model.at(word.substr(start, end - start)).first;
tokens.insert(tokens.begin(), token_id);
int next_start = best_segmentations_starts[start];
end = start;
start = next_start;
}
const auto token_id = model.at(word.substr(start, end - start)).first;
tokens.insert(tokens.begin(), token_id);
return std::make_pair(tokens, score);
}
bool replit_tokenizer_load(mpt_vocab & tokenizer, gguf_context * ggufctx, int tokens_keyidx, int max_vocab_size) {
int scores_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.scores");
if (scores_keyidx == -1) {
fprintf(stderr, "%s: llama token scores not found!\n", __func__);
return false;
}
const auto *scores = reinterpret_cast<const float *>(gguf_get_arr_data(ggufctx, scores_keyidx));
for (LLModel::Token i = 0; i < max_vocab_size; i++) {
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
tokenizer.piece_map[word] = std::make_pair(i, -scores[i]);
tokenizer.raw.id_to_token[i] = word;
tokenizer.raw.token_to_id[word] = i;
}
return true;
}
std::string replace_all(const std::string & str, // where to work
const std::string & find, // substitute 'find'
const std::string & replace // by 'replace'
) {
std::string result;
size_t find_len = find.size();
size_t pos, from = 0;
while (std::string::npos != (pos = str.find(find, from))) {
result.append(str, from, pos - from);
result.append(replace);
from = pos + find_len;
}
result.append(str, from, std::string::npos);
return result;
}
std::vector<LLModel::Token> replit_tokenizer_tokenize(mpt_vocab & tokenizer, const std::string & text) {
std::vector<LLModel::Token> tokens;
auto normalized_text = replace_all(text, " ", replit_ws_symbol);
auto tokenized = encode_word(normalized_text, tokenizer.piece_map);
return tokenized.first;
}
std::string replit_tokenizer_detokenize(mpt_vocab & tokenizer, const std::vector<LLModel::Token> & tokens) {
std::string text;
for (auto token : tokens) {
text += tokenizer.raw.id_to_token[token];
}
return replace_all(text, replit_ws_symbol, " ");
}
static bool kv_cache_init(
const struct mpt_hparams & hparams,
struct llm_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int64_t n_mem = (int64_t)n_layer*n_ctx;
const int64_t n_elements = n_embd*n_mem;
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
struct ggml_init_params params;
params.mem_size = cache.buf.size;
params.mem_buffer = cache.buf.addr;
params.no_alloc = false;
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
return true;
}
// load the model's weights from a file path. if mem_req ptr is passed the model is
// only partially parsed to estimate required memory
bool mpt_model_load(const std::string &fname, mpt_model & model, mpt_vocab & vocab, size_t * mem_req) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
if (mem_req != nullptr) {
*mem_req = 0;
}
// create the ggml context
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ &model.ctx,
};
gguf_context *ggufctx = gguf_init_from_file(fname.c_str(), params);
if (!ggufctx) {
fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
return false;
}
printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
// print some standard metadata
{
int keyidx;
keyidx = gguf_find_key(ggufctx, "general.name");
if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.description");
if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.author");
if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.license");
if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.architecture");
if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.file_type");
if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
}
// check required metadata
{
// check model architecture kv
int keyidx = gguf_find_key(ggufctx, "general.architecture");
if (keyidx == -1) {
fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
return false;
}
if (strcmp(gguf_get_val_str(ggufctx, keyidx), "mpt") != 0) {
fprintf(stderr, "%s: model architecture not supported!\n", __func__);
return false;
}
}
// load hparams
{
auto & hparams = model.hparams;
bool ok = false;
int keyidx;
do {
keyidx = gguf_find_key(ggufctx, "mpt.context_length");
if (keyidx == -1) { break; }
hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx);
keyidx = gguf_find_key(ggufctx, "mpt.embedding_length");
if (keyidx == -1) { break; }
hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
keyidx = gguf_find_key(ggufctx, "mpt.attention.head_count");
if (keyidx == -1) { break; }
hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
keyidx = gguf_find_key(ggufctx, "mpt.block_count");
if (keyidx == -1) { break; }
hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
keyidx = gguf_find_key(ggufctx, "mpt.attention.max_alibi_bias");
if (keyidx == -1) { break; }
hparams.alibi_bias_max = gguf_get_val_f32(ggufctx, keyidx);
keyidx = gguf_find_key(ggufctx, "mpt.attention.clamp_kqv");
if (keyidx != -1) { // optional
hparams.clip_qkv = gguf_get_val_f32(ggufctx, keyidx);
}
keyidx = gguf_find_key(ggufctx, "mpt.attention.layer_norm_epsilon");
if (keyidx == -1) { break; }
hparams.norm_eps = gguf_get_val_f32(ggufctx, keyidx);
ok = true;
} while (false);
if (!ok) {
fprintf(stderr, "%s: required hparam missing!\n", __func__);
return false;
}
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: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
}
// load vocab
{
auto & hparams = model.hparams;
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
if (tokens_keyidx == -1) {
fprintf(stderr, "%s: tokenizer vocab not found!\n", __func__);
return false;
}
int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
if (keyidx == -1) {
fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
return false;
}
std::string tokenizer_model(gguf_get_val_str(ggufctx, keyidx));
hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
printf("%s: %s tokenizer vocab = %d\n", __func__, tokenizer_model.c_str(), int(hparams.n_vocab));
if (tokenizer_model == "llama") { // Replit
vocab.is_replit = true;
if (!replit_tokenizer_load(vocab, ggufctx, tokens_keyidx, hparams.n_vocab)) {
return false;
}
} else if (tokenizer_model == "gpt2") {
int toktypes_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.token_type");
if (toktypes_keyidx == -1) {
fprintf(stderr, "%s: gpt2 token types not found!\n", __func__);
return false;
}
const auto *toktypes = reinterpret_cast<const uint32_t *>(gguf_get_arr_data(ggufctx, toktypes_keyidx));
for (int i = 0; i < hparams.n_vocab; i++) {
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
bool special = false;
if (toktypes[i] == MPT_TOKEN_TYPE_CONTROL) {
special = true;
} else if (toktypes[i] != MPT_TOKEN_TYPE_NORMAL) {
fprintf(stderr, "%s: unknown token type: %d\n", __func__, int(toktypes[i]));
return false;
}
vocab.raw.token_to_id[word] = i;
vocab.raw.id_to_token[i] = word;
if (special) {
vocab.raw.add_special_token(word);
}
}
} else {
fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
return false;
}
}
auto & ctx = model.ctx;
size_t ctx_size = ggml_get_mem_size(ctx);
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
if (mem_req != nullptr) {
*mem_req = ctx_size;
gguf_free(ggufctx);
return false;
}
// prepare memory for the weights
{
const auto & hparams = model.hparams;
model.layers.resize(hparams.n_layer);
model.wte = ggml_get_tensor(ctx, "token_embd.weight");
model.norm_f_w = ggml_get_tensor(ctx, "output_norm.weight");
auto name = [](int i, std::string n) {
static std::string key;
key = "blk." + std::to_string(i) + "." + n;
return key.c_str();
};
for (int i = 0; i < hparams.n_layer; ++i) {
auto &layer = model.layers[i];
layer.norm_1_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
layer.norm_2_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
layer.attn_Wqkv_w = ggml_get_tensor(ctx, name(i, "attn_qkv.weight"));
layer.attn_out_proj_w = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
layer.ffn_up_proj_w = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
layer.ffn_down_proj_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
}
}
// key + value memory
{
const auto &hparams = model.hparams;
if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
ggml_free(ctx);
return false;
}
const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
model.scr0_buf.resize(256u * 1024 * 1024);
model.scr1_buf.resize(256u * 1024 * 1024);
return true;
}
bool mpt_eval(
mpt_model & model,
const int n_threads,
const int n_past,
const std::vector<int> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
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;
const size_t init_buf_size = 1024_MiB;
if (!model.eval_buf.addr || model.eval_buf.size < init_buf_size)
model.eval_buf.resize(init_buf_size);
if (mem_per_token > 0 && mem_per_token*N > model.eval_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__, model.buf.size, buf_size_new);
// reallocate
model.eval_buf.resize(buf_size_new);
if (model.eval_buf.addr == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.eval_buf.size);
return false;
}
}
struct ggml_init_params params = {
.mem_size = model.eval_buf.size,
.mem_buffer = model.eval_buf.addr,
.no_alloc = false
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
// wte
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
for (int il = 0; il < n_layer; ++il) {
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur = inpSA;
// self-attention
{
// norm1
cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].norm_1_w, cur),
cur);
// compute QKV
cur = ggml_mul_mat(ctx0,
model.layers[il].attn_Wqkv_w,
cur);
// TODO: clip_qkv
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*ggml_element_size(cur)*n_embd));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*ggml_element_size(cur)*n_embd));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*ggml_element_size(cur)*n_embd));
// TODO: qk_ln? (seems to be False in MPT-7B configs)
{
Vcur = ggml_transpose(ctx0, Vcur);
struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, model.kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(model.kv_self.v),
(il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, N),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// Alibi
struct ggml_tensor * KQ_scaled_biased = ggml_alibi(
ctx0, ggml_cont(ctx0, KQ_scaled), n_past, n_head, model.hparams.alibi_bias_max
);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_biased, n_past);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
struct ggml_tensor * V =
ggml_view_3d(ctx0, model.kv_self.v,
n_past + N, n_embd/n_head, n_head,
n_ctx*ggml_element_size(model.kv_self.v),
n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head,
il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd);
// KQV = transpose(V) * KQ_soft_max
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model.layers[il].attn_out_proj_w,
cur);
}
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
// residual
struct ggml_tensor * resSA = ggml_add(ctx0, cur, inpSA);
// feed-forward network
{
cur = resSA;
// norm2
cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].norm_2_w, cur),
cur);
// ffn
cur = ggml_mul_mat(ctx0,
model.layers[il].ffn_up_proj_w,
cur);
cur = ggml_gelu(ctx0, cur);
cur = ggml_mul_mat(ctx0,
model.layers[il].ffn_down_proj_w,
cur);
}
// self-attention + FF
inpL = ggml_add(ctx0, cur, resSA);
}
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
struct ggml_tensor * out = inpL;
// -> logits
{
out = ggml_norm(ctx0, out, model.hparams.norm_eps);
out = ggml_mul(ctx0,
ggml_repeat(ctx0, model.norm_f_w, out),
out);
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
out = ggml_mul_mat(ctx0, model.wte, out);
}
ggml_build_forward_expand(&gf, out);
// run the computation
{
std::unique_ptr<uint8_t []> data;
auto plan = ggml_graph_plan(&gf, n_threads);
if (plan.work_size > 0) {
data.reset(new uint8_t[plan.work_size]);
plan.work_data = data.get();
}
ggml_graph_compute(&gf, &plan);
}
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(out) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
ggml_free(ctx0);
return true;
}
#define MPT_MAX_RNG_STATE 64*1024
size_t mpt_get_state_size(const mpt_model &model)
{
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = MPT_MAX_RNG_STATE;
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = model.kv_self.buf.size;
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
fflush(stdout);
return s_total;
}
size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint8_t *dest)
{
uint8_t * out = dest;
fflush(stdout);
// copy rng
{
std::stringstream rng_ss;
rng_ss << rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[MPT_MAX_RNG_STATE];
memset(&rng_buf[0], 0, MPT_MAX_RNG_STATE);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
memcpy(out, &rng_buf[0], MPT_MAX_RNG_STATE); out += MPT_MAX_RNG_STATE;
}
// copy kv cache
{
const size_t kv_size = model.kv_self.buf.size;
const int kv_ntok = model.kv_self.n;
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
if (kv_size) {
memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
}
}
const size_t written = out - dest;
assert(written == mpt_get_state_size(model));
fflush(stdout);
return written;
}
size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
{
const uint8_t * in = src;
// set rng
{
size_t rng_size;
char rng_buf[MPT_MAX_RNG_STATE];
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
memcpy(&rng_buf[0], in, MPT_MAX_RNG_STATE); in += MPT_MAX_RNG_STATE;
std::stringstream rng_ss;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> *rng;
assert(rng_ss.fail() == false);
}
// set kv cache
{
size_t kv_size;
int kv_ntok;
memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
if (kv_size) {
assert(model->kv_self.buf.size == kv_size);
void * k_data = model->kv_self.k->data; // remember data pointers
void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
model->kv_self.k->data = k_data; // restore correct data pointers
model->kv_self.v->data = v_data;
}
model->kv_self.n = kv_ntok;
}
const size_t nread = in - src;
assert(nread == mpt_get_state_size(*model));
fflush(stdout);
return nread;
}
struct MPTPrivate {
const std::string modelPath;
bool modelLoaded;
mpt_vocab vocab;
mpt_model *model = nullptr;
int64_t n_threads = 0;
size_t mem_per_token = 0;
std::mt19937 rng;
bool has_end_of_text = false;
};
MPT::MPT()
: d_ptr(new MPTPrivate) {
d_ptr->model = new mpt_model;
d_ptr->model->ctx = nullptr;
d_ptr->modelLoaded = false;
}
size_t MPT::requiredMem(const std::string &modelPath) {
mpt_model dummy_model;
mpt_vocab dummy_vocab;
size_t mem_req;
mpt_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
return mem_req;
}
bool MPT::loadModel(const std::string &modelPath) {
std::mt19937 rng(time(NULL));
d_ptr->rng = rng;
// load the model
if (!mpt_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) {
std::cerr << "MPT ERROR: failed to load model from " << modelPath;
return false;
}
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
d_ptr->modelLoaded = true;
const auto & vocab = d_ptr->vocab;
d_ptr->has_end_of_text = vocab.raw.token_to_id.find(vocab.end_of_text()) != vocab.raw.token_to_id.end();
fflush(stdout);
return true;
}
void MPT::setThreadCount(int32_t n_threads) {
d_ptr->n_threads = n_threads;
}
int32_t MPT::threadCount() const
{
return d_ptr->n_threads;
}
MPT::~MPT()
{
delete d_ptr->model;
}
bool MPT::isModelLoaded() const
{
return d_ptr->modelLoaded;
}
size_t MPT::stateSize() const
{
return mpt_get_state_size(*d_ptr->model);
}
size_t MPT::saveState(uint8_t *dest) const
{
return mpt_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
}
size_t MPT::restoreState(const uint8_t *src)
{
return mpt_set_state_data(d_ptr->model, &d_ptr->rng, src);
}
std::vector<LLModel::Token> MPT::tokenize(PromptContext &, const std::string &str) const
{
if (d_ptr->vocab.is_replit) {
return replit_tokenizer_tokenize(d_ptr->vocab, str);
}
return ::gpt_tokenize(d_ptr->vocab.raw, str);
}
std::string MPT::tokenToString(Token id) const
{
if (d_ptr->vocab.is_replit) {
return replit_tokenizer_detokenize(d_ptr->vocab, {id});
}
return d_ptr->vocab.raw.id_to_token[id];
}
LLModel::Token MPT::sampleToken(PromptContext &promptCtx) const
{
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
n_prev_toks,
promptCtx.logits,
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
promptCtx.repeat_penalty,
d_ptr->rng);
}
bool MPT::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
{
// determine the required inference memory per token:
static bool initialized = false;
if (!initialized) {
mpt_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
d_ptr->mem_per_token);
initialized = true;
}
return mpt_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
}
int32_t MPT::contextLength() const
{
return d_ptr->model->hparams.n_ctx;
}
const std::vector<LLModel::Token> &MPT::endTokens() const
{
static std::vector<LLModel::Token> fres;
if (fres.empty()) {
fres = {0, d_ptr->vocab.raw.token_to_id[d_ptr->vocab.end_of_text()]};
}
return fres;
}
std::string get_arch_name(gguf_context *ctx_gguf) {
std::string arch_name;
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
if (ktype != GGUF_TYPE_STRING) {
throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
}
return gguf_get_val_str(ctx_gguf, kid);
}
#if defined(_WIN32)
#define DLL_EXPORT __declspec(dllexport)
#else
#define DLL_EXPORT __attribute__ ((visibility ("default")))
#endif
extern "C" {
DLL_EXPORT bool is_g4a_backend_model_implementation() {
return true;
}
DLL_EXPORT const char *get_model_type() {
return modelType_;
}
DLL_EXPORT const char *get_build_variant() {
return GGML_BUILD_VARIANT;
}
DLL_EXPORT bool magic_match(const char * fname) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
if (!ctx_gguf)
return false;
bool isValid = gguf_get_version(ctx_gguf) <= 2;
isValid = isValid && get_arch_name(ctx_gguf) == "mpt";
gguf_free(ctx_gguf);
return isValid;
}
DLL_EXPORT LLModel *construct() {
return new MPT;
}
}

41
gpt4all-backend/mpt_impl.h
View File

@ -1,41 +0,0 @@
#ifndef MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#error This file is NOT meant to be included outside of mpt.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#endif
#ifndef MPT_H
#define MPT_H
#include <string>
#include <functional>
#include <vector>
#include "llmodel.h"
struct MPTPrivate;
class MPT : public LLModel {
public:
MPT();
~MPT();
bool supportsEmbedding() const override { return false; }
bool supportsCompletion() const override { return true; }
bool loadModel(const std::string &modelPath) override;
bool isModelLoaded() const override;
size_t requiredMem(const std::string &modelPath) override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
private:
MPTPrivate *d_ptr;
protected:
std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
std::string tokenToString(Token) const override;
Token sampleToken(PromptContext &ctx) const override;
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
int32_t contextLength() const override;
const std::vector<Token>& endTokens() const override;
};
#endif // MPT_H

28
gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py
View File

@ -18,7 +18,7 @@ from pathlib import Path
import gguf
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, MptConfig
from transformers.models.gpt2 import tokenization_gpt2
@ -30,7 +30,7 @@ if not 3 <= len(sys.argv) < 5:
print(" ftype == 1 -> float16")
sys.exit(1)
model_name = sys.argv[1]
dir_model = Path(sys.argv[1])
dir_out = Path(sys.argv[2])
# make sure the output directory exists
@ -50,7 +50,7 @@ if len(sys.argv) > 3:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = dir_out / f"ggml-model-{Path(model_name).name}-{ftype_str[ftype]}.gguf"
fname_out = dir_out / f"ggml-model-{dir_model.name}-{ftype_str[ftype]}.gguf"
ARCH = gguf.MODEL_ARCH.MPT
@ -58,7 +58,7 @@ gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
print("gguf: get model metadata")
config = AutoConfig.from_pretrained(model_name)
config = AutoConfig.from_pretrained(dir_model, trust_remote_code=True)
block_count = config.n_layers
gguf_writer.add_name("MPT")
@ -67,17 +67,19 @@ gguf_writer.add_embedding_length(config.d_model)
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(4 * config.d_model)
gguf_writer.add_head_count(config.n_heads)
gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
if kv_n_heads := config.attn_config.get('kv_n_heads'):
gguf_writer.add_head_count_kv(kv_n_heads)
gguf_writer.add_max_alibi_bias(config.attn_config['alibi_bias_max'])
gguf_writer.add_layer_norm_eps(MptConfig().layer_norm_epsilon) # use default from upstream transformers
gguf_writer.add_file_type(ftype)
clip_qkv = config.attn_config.clip_qkv
clip_qkv = config.attn_config['clip_qkv']
if clip_qkv is not None:
gguf_writer.add_clamp_kqv(clip_qkv)
print("gguf: get gpt2 tokenizer vocab")
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(dir_model)
special_ids = tokenizer.all_special_ids
@ -111,13 +113,17 @@ gguf_writer.add_tokenizer_model("gpt2")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
special_vocab.add_to_gguf(gguf_writer)
print("gguf: get tensor metadata")
print("Loading model:", model_name)
print("Loading model:", dir_model)
model = AutoModelForCausalLM.from_pretrained(
model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True,
dir_model, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32,
low_cpu_mem_usage=True, trust_remote_code=True,
)
print("Model loaded:", model_name)
print("Model loaded:", dir_model)
tensor_map = gguf.get_tensor_name_map(ARCH, block_count)

2
gpt4all-chat/CMakeLists.txt
View File

@ -189,8 +189,6 @@ install(TARGETS llamamodel-mainline-default DESTINATION lib COMPONENT ${COMPONEN
if(APPLE)
install(TARGETS llamamodel-mainline-metal DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
endif()
install(TARGETS mpt-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
install(TARGETS mpt-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
install(TARGETS bert-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})
install(TARGETS bert-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN})

3
gpt4all-chat/chatllm.cpp
View File

@ -9,7 +9,6 @@
//#define DEBUG
//#define DEBUG_MODEL_LOADING
#define MPT_INTERNAL_STATE_VERSION 0
#define GPTJ_INTERNAL_STATE_VERSION 0
#define LLAMA_INTERNAL_STATE_VERSION 0
#define BERT_INTERNAL_STATE_VERSION 0
@ -325,7 +324,6 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo)
switch (m_llModelInfo.model->implementation().modelType()[0]) {
case 'L': m_llModelType = LLModelType::LLAMA_; break;
case 'G': m_llModelType = LLModelType::GPTJ_; break;
case 'M': m_llModelType = LLModelType::MPT_; break;
case 'B': m_llModelType = LLModelType::BERT_; break;
default:
{
@ -760,7 +758,6 @@ bool ChatLLM::serialize(QDataStream &stream, int version, bool serializeKV)
if (version > 1) {
stream << m_llModelType;
switch (m_llModelType) {
case MPT_: stream << MPT_INTERNAL_STATE_VERSION; break;
case GPTJ_: stream << GPTJ_INTERNAL_STATE_VERSION; break;
case LLAMA_: stream << LLAMA_INTERNAL_STATE_VERSION; break;
case BERT_: stream << BERT_INTERNAL_STATE_VERSION; break;

1
gpt4all-chat/chatllm.h
View File

@ -10,7 +10,6 @@
#include "../gpt4all-backend/llmodel.h"
enum LLModelType {
MPT_,
GPTJ_,
LLAMA_,
CHATGPT_,

10
gpt4all-chat/metadata/models2.json
View File

@ -126,20 +126,20 @@
},
{
"order": "j",
"md5sum": "51c627fac9062e208f9b386f105cbd48",
"md5sum": "e30579a1b109882f10e2a5e75ea388fb",
"disableGUI": "true",
"name": "Replit",
"filename": "replit-code-v1-3b-q4_0.gguf",
"filesize": "1532949760",
"filename": "replit-code-v1_5-3b-q4_0.gguf",
"filesize": "1870449696",
"requires": "2.5.0",
"ramrequired": "4",
"parameters": "3 billion",
"quant": "f16",
"quant": "q4_0",
"type": "Replit",
"systemPrompt": " ",
"promptTemplate": "%1",
"description": "<strong>Trained on subset of the Stack</strong><br><ul><li>Code completion based<li>Licensed for commercial use</ul>",
"url": "https://gpt4all.io/models/gguf/replit-code-v1-3b-q4_0.gguf"
"url": "https://gpt4all.io/models/gguf/replit-code-v1_5-3b-q4_0.gguf"
},
{
"order": "k",