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backend: use llamamodel.cpp for Falcon

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Cebtenzzre 2023-09-29 13:20:07 -04:00 committed by Adam Treat
parent cca9e6ce81
commit ce7be1db48
5 changed files with 1 additions and 1180 deletions
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5
gpt4all-backend/CMakeLists.txt
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@ -103,11 +103,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS)
# gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
# prepare_target(gptj ggml-230511)
add_library(falcon-${BUILD_VARIANT} SHARED
falcon.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
target_compile_definitions(falcon-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999)
prepare_target(falcon 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)

989
gpt4all-backend/falcon.cpp
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@ -1,989 +0,0 @@
#include "ggml.h"
#define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#include "falcon_impl.h"
#include "llama.h"
#include "utils.h"
#include "llmodel_shared.h"
#include <stdio.h>
#include <string.h>
#include <cassert>
#include <cinttypes>
#include <iostream>
#include <sstream>
namespace {
const char *modelType_ = "Falcon";
}
// commented out 40B support as it presently would require forking ggml/llama.cpp
// can re-add once mainline ggml supports it
#define FALCON_MAGIC 0x67676a74
// default hparams (Falcon 7B)
struct falcon_hparams {
int32_t n_vocab = 65024;
int32_t n_embd = 4544;
int32_t n_head = 71;
int32_t n_head_kv = 1;
int32_t n_layer = 32;
int32_t falcon_version = 7; // 7 for Falcon-7B, 40 for Falcon-40B
int32_t ftype = 1;
int32_t n_ctx = 2048;
};
struct falcon_layer {
// normalization
struct ggml_tensor* input_layernorm;
struct ggml_tensor* input_layernorm_b;
//struct ggml_tensor* attention_norm; // Falcon-40B only
//struct ggml_tensor* attention_norm_b; // Falcon-40B only
// attention
struct ggml_tensor* query_key_value;
struct ggml_tensor* wo;
// ff
struct ggml_tensor* ffn_up;
struct ggml_tensor* ffn_down;
};
struct falcon_model {
falcon_hparams hparams;
struct ggml_tensor* tok_embeddings;
struct ggml_tensor* output_norm;
struct ggml_tensor* output_norm_b;
struct ggml_tensor* lm_head;
std::vector<falcon_layer> layers;
// key + value memory
llm_kv_cache kv_self;
struct ggml_context* ctx;
std::map<std::string, struct ggml_tensor*> tensors;
llm_buffer eval_buf;
llm_buffer work_buf;
llm_buffer scr0_buf;
llm_buffer scr1_buf;
};
static bool kv_cache_init(
const struct falcon_hparams & hparams,
struct llm_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int n_embd = hparams.n_embd;
const int dim_head = n_embd / hparams.n_head;
const int dim_kv = dim_head * hparams.n_head_kv;
const int n_layer = hparams.n_layer;
const int64_t n_mem = (int64_t)n_layer*n_ctx;
const int64_t n_elements = dim_kv * 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
bool falcon_model_load(const std::string & fname, falcon_model & model, gpt_vocab & vocab, size_t *mem_req) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
if (mem_req) {
*mem_req = 0;
}
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
return false;
}
// verify magic
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != FALCON_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
}
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
// load hparams
{
auto & hparams = model.hparams;
fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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_head_kv, sizeof(hparams.n_head_kv));
fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
fin.read((char *) &hparams.falcon_version, sizeof(hparams.falcon_version));
fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
if (hparams.falcon_version != 7) { // && hparams.falcon_version != 40) {
fprintf(stderr, "%s: invalid model file '%s' (bad Falcon version: %d)\n", __func__, fname.c_str(), hparams.falcon_version);
return false;
}
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv);
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
printf("%s: ftype = %d\n", __func__, hparams.ftype);
printf("%s: qntvr = %d\n", __func__, qntvr);
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
}
// load vocab
{
const int32_t n_vocab = model.hparams.n_vocab;
std::string word;
std::vector<char> buf(128);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
buf.resize(len);
fin.read((char *) buf.data(), len);
word.assign(buf.data(), len);
uint32_t dummy;
fin.read((char *) &dummy, sizeof(dummy));
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 or quantized
// in order to save memory and also to speed up the computation
ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
if (wtype == GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
__func__, fname.c_str(), model.hparams.ftype);
return false;
}
auto & ctx = model.ctx;
size_t ctx_size = 0;
{
const auto& hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_head = hparams.n_head;
const int n_head_kv = hparams.n_head_kv;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_ff = 4 * model.hparams.n_embd;
const int n_vocab = hparams.n_vocab;
const int head_dim = hparams.n_embd / hparams.n_head;
ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // tok_embeddings
ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm
ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm_b
ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // lm_head
// if (hparams.version == 40) { // Falcon-40B
// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm
// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm_b
// }
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm_b
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * (n_head_kv * 2 + n_head) * head_dim); // query_key_value
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_embd); // wo
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_ff); // ffn_up
ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_ff * n_embd); // ffn_down
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
if (mem_req) {
const int n_embd = model.hparams.n_embd;
const int dim_head = n_embd / model.hparams.n_head;
const int dim_kv = dim_head * model.hparams.n_head_kv;
const int n_layer = model.hparams.n_layer;
const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx;
const int64_t n_elements = dim_kv * n_mem;
size_t kv_cache_size = 2u*n_elements*ggml_type_size(wtype) + 2_MiB;
*mem_req = ctx_size + kv_cache_size;
return false;
}
// create the ggml context
{
struct ggml_init_params params = {
.mem_size = ctx_size,
.mem_buffer = NULL,
.no_alloc = false,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
}
// prepare memory for the weights
{
const auto& hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_head = hparams.n_head;
const int n_head_kv = hparams.n_head_kv;
const int n_layer = hparams.n_layer;
const int n_ff = 4 * model.hparams.n_embd;
const int n_vocab = hparams.n_vocab;
const int head_dim = hparams.n_embd / hparams.n_head;
model.layers.resize(n_layer);
model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
// map by name
model.tensors["transformer.word_embeddings.weight"] =
model.tok_embeddings;
model.tensors["transformer.ln_f.weight"] = model.output_norm;
model.tensors["transformer.ln_f.bias"] = model.output_norm_b;
model.tensors["lm_head.weight"] = model.lm_head;
for (int i = 0; i < n_layer; ++i) {
auto& layer = model.layers[i];
layer.input_layernorm =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.input_layernorm_b =
ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
// if (hparams.version == 40) { // for Falcon-40B only
// layer.attention_norm =
// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
// layer.attention_norm_b =
// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
// }
// query_key_value shape for config.multi_query == True:
layer.query_key_value = ggml_new_tensor_2d(
ctx, wtype, n_embd, (n_head_kv * 2 + n_head) * head_dim);
layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
layer.ffn_up = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
layer.ffn_down = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
// map by name
// if (hparams.version == 40) {
// // Falcon-40B:
// model.tensors["transformer.h." + std::to_string(i) +
// ".ln_mlp.weight"] = layer.input_layernorm;
// model.tensors["transformer.h." + std::to_string(i) +
// ".ln_mlp.bias"] = layer.input_layernorm_b;
// model.tensors["transformer.h." + std::to_string(i) +
// ".ln_attn.weight"] = layer.attention_norm;
// model.tensors["transformer.h." + std::to_string(i) +
// ".ln_attn.bias"] = layer.attention_norm_b;
// } else {
// Falcon-7B:
model.tensors["transformer.h." + std::to_string(i) +
".input_layernorm.weight"] = layer.input_layernorm;
model.tensors["transformer.h." + std::to_string(i) +
".input_layernorm.bias"] = layer.input_layernorm_b;
//}
model.tensors["transformer.h." + std::to_string(i) +
".self_attention.query_key_value.weight"] =
layer.query_key_value;
model.tensors["transformer.h." + std::to_string(i) +
".self_attention.dense.weight"] = layer.wo;
model.tensors["transformer.h." + std::to_string(i) +
".mlp.dense_h_to_4h.weight"] = layer.ffn_up;
model.tensors["transformer.h." + std::to_string(i) +
".mlp.dense_4h_to_h.weight"] = layer.ffn_down;
}
}
// key + value memory
{
const auto & hparams = model.hparams;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_head_kv = hparams.n_head_kv;
const int head_dim = hparams.n_embd / hparams.n_head;
const int64_t n_mem = n_layer*n_ctx;
const int64_t n_elements = head_dim*n_mem;
if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, 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: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
// load weights
{
int n_tensors = 0;
size_t total_size = 0;
printf("%s: ", __func__);
while (true) {
int32_t n_dims;
int32_t length;
int32_t ttype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
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);
fin.seekg(-static_cast<ptrdiff_t>(fin.tellg()) & 31, std::ios_base::cur);
if (model.tensors.find(name.data()) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
return false;
}
auto tensor = model.tensors[name.data()];
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n",
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
return false;
}
// for debugging
if (0) {
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
}
const size_t bpe = ggml_type_size(ggml_type(ttype));
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
return false;
}
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
total_size += ggml_nbytes(tensor);
if (++n_tensors % 8 == 0) {
printf(".");
fflush(stdout);
}
}
printf(" done\n");
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
}
fin.close();
model.eval_buf.resize(1280u * 1024 * 1024);
model.scr0_buf.resize(256u * 1024 * 1024);
model.scr1_buf.resize(256u * 1024 * 1024);
return true;
}
// 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 falcon_eval(
falcon_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) {
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_head_kv = hparams.n_head_kv;
const int n_vocab = hparams.n_vocab;
const int version = hparams.falcon_version;
const size_t head_dim = n_embd / n_head;
struct ggml_init_params eval_ctx_params = {
.mem_size = model.eval_buf.size,
.mem_buffer = model.eval_buf.addr,
.no_alloc = false,
};
struct ggml_context * ctx0 = ggml_init(eval_ctx_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.tok_embeddings, embd);
struct ggml_tensor* repeat_dummy = ggml_new_tensor_3d(ctx0, inpL->type, head_dim, N + n_past, n_head);
ggml_type wtype = GGML_TYPE_F32;
const int sizeof_wtype = ggml_type_sizef(wtype);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * cur;
struct ggml_tensor * layernorm_output;
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
// self-attention
{
layernorm_output = ggml_norm(ctx0, inpL, 1e-5f);
layernorm_output = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].input_layernorm, layernorm_output),
layernorm_output),
ggml_repeat(ctx0, model.layers[il].input_layernorm_b, layernorm_output));
// if (version == 40) { // Falcon-40B only
// cur = ggml_norm(ctx0, inpL);
// cur = ggml_add(ctx0,
// ggml_mul(ctx0,
// ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
// cur),
// ggml_repeat(ctx0, model.layers[il].attention_norm_b, cur));
// }
// else {
cur = layernorm_output;
// }
// compute QKV
cur = ggml_mul_mat(ctx0, model.layers[il].query_key_value, cur);
// Note that the strides for Kcur, Vcur are set up so that the
// resulting views are misaligned with the tensor's storage
// (by applying the K/V offset we shift the tensor's original
// view to stick out behind the viewed QKV tensor's allocated
// memory, so to say). This is ok because no actual accesses
// happen to that out-of-range memory, but it can require some
// trickery when trying to accurately dump these views for
// debugging.
struct ggml_tensor * Qcur = ggml_view_3d(
ctx0, cur, head_dim, n_head, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
0);
struct ggml_tensor * Kcur = ggml_view_3d(
ctx0, cur, head_dim, n_head_kv, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
head_dim * n_head * sizeof_wtype);
struct ggml_tensor * Vcur = ggml_view_3d(
ctx0, cur, head_dim, n_head_kv, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
head_dim * (n_head + n_head_kv) * sizeof_wtype);
// using mode = 2 for neox mode
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, head_dim, 2, n_ctx);
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, n_ctx);
// store key and value to memory
{
struct ggml_tensor* k = ggml_view_1d(
ctx0, model.kv_self.k, N * n_head_kv * head_dim,
(ggml_element_size(model.kv_self.k) * n_head_kv * head_dim) *
(il * n_ctx + n_past));
struct ggml_tensor* v = ggml_view_1d(
ctx0, model.kv_self.v, N * n_head_kv * head_dim,
(ggml_element_size(model.kv_self.v) * n_head_kv * head_dim) *
(il * n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
struct ggml_tensor * K = ggml_permute(
ctx0,
ggml_view_3d(
ctx0,
model.kv_self.k,
head_dim, n_head_kv, n_past + N,
head_dim * sizeof_wtype,
head_dim * n_head_kv * sizeof_wtype,
il * n_ctx * ggml_element_size(model.kv_self.k) * n_head_kv * head_dim),
0, 2, 1, 3);
// K * Q
// changed from repeat2 back to repeat, will not support 40B!
K = ggml_cont(ctx0, ggml_repeat(ctx0, K, repeat_dummy));
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(head_dim)))
);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(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_permute(
ctx0,
ggml_view_3d(
ctx0,
model.kv_self.v,
head_dim, n_head_kv, n_past + N,
head_dim * sizeof_wtype,
head_dim * n_head_kv * sizeof_wtype,
il * n_ctx * ggml_element_size(model.kv_self.v) * n_head_kv * head_dim),
0, 2, 1, 3);
// changed from repeat2 back to repeat, will not support 40B!
V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat(ctx0, V, repeat_dummy)));
// 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
{
cur = ggml_mul_mat(ctx0,
model.layers[il].wo,
cur);
}
}
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
struct ggml_tensor* inpFF = layernorm_output;
struct ggml_tensor* attn_out = ggml_cpy(
ctx0, cur, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
{
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up, inpFF);
cur = ggml_gelu(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
}
cur = ggml_add(ctx0, cur, attn_out);
cur = ggml_add(ctx0, cur, inpL);
// input for next layer
inpL = cur;
}
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
// norm
{
inpL = ggml_norm(ctx0, inpL, 1e-5f);
// inpL = ln_f_g*inpL + ln_f_b
inpL = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.output_norm, inpL),
inpL),
ggml_repeat(ctx0, model.output_norm_b, inpL));
}
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
// lm_head
{
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
//inpL = ggml_add(ctx0,
// ggml_repeat(ctx0, model.lmh_b, inpL),
// inpL);
}
// logits -> probs
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute_g4a(model.work_buf, &gf, n_threads);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (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 MAX_RNG_STATE 64*1024
size_t falcon_get_state_size(const falcon_model &model) {
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = 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
);
return s_total;
}
size_t falcon_copy_state_data(const falcon_model &model, const std::mt19937 &rng, uint8_t *dest)
{
uint8_t * out = dest;
// copy rng
{
std::stringstream rng_ss;
rng_ss << rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[MAX_RNG_STATE];
memset(&rng_buf[0], 0, 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], MAX_RNG_STATE); out += 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 == falcon_get_state_size(model));
fflush(stdout);
return written;
}
size_t falcon_set_state_data(falcon_model *model, std::mt19937 *rng, const uint8_t *src)
{
const uint8_t * in = src;
// set rng
{
size_t rng_size;
char rng_buf[MAX_RNG_STATE];
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
memcpy(&rng_buf[0], in, MAX_RNG_STATE); in += 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 == falcon_get_state_size(*model));
fflush(stdout);
return nread;
}
struct FalconPrivate {
const std::string modelPath;
bool modelLoaded;
gpt_vocab vocab;
falcon_model *model = nullptr;
int64_t n_threads = 0;
size_t mem_per_token = 0;
std::mt19937 rng;
};
Falcon::Falcon() : d_ptr(new FalconPrivate) {
d_ptr->model = new falcon_model;
d_ptr->model->ctx = nullptr;
d_ptr->modelLoaded = false;
}
Falcon::~Falcon() {
if(d_ptr->model->ctx) {
ggml_free(d_ptr->model->ctx);
d_ptr->model->ctx = nullptr;
}
delete d_ptr->model;
}
bool Falcon::loadModel(const std::string &modelPath)
{
std::mt19937 rng(time(NULL));
d_ptr->rng = rng;
// load the model
if (!falcon_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) {
std::cerr << "FALCON 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;
fflush(stdout);
return true;
}
bool Falcon::isModelLoaded() const
{
return d_ptr -> modelLoaded;
}
size_t Falcon::requiredMem(const std::string &modelPath)
{
falcon_model dummy_model;
gpt_vocab dummy_vocab;
size_t mem_req;
auto fin = std::ifstream(modelPath, std::ios::binary);
falcon_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
return mem_req;
}
size_t Falcon::stateSize() const
{
return falcon_get_state_size(*d_ptr->model);
}
size_t Falcon::saveState(uint8_t *dest) const
{
return falcon_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
}
size_t Falcon::restoreState(const uint8_t *src)
{
return falcon_set_state_data(d_ptr->model, &d_ptr->rng, src);
}
void Falcon::setThreadCount(int32_t n_threads)
{
d_ptr->n_threads = n_threads;
}
int32_t Falcon::threadCount() const
{
return d_ptr->n_threads;
}
std::vector<LLModel::Token> Falcon::tokenize(PromptContext &, const std::string &str) const
{
return ::gpt_tokenize(d_ptr->vocab, str);
}
LLModel::Token Falcon::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);
}
std::string Falcon::tokenToString(Token id) const
{
return d_ptr->vocab.id_to_token[id];
}
bool Falcon::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
{
// determine the required inference memory per token:
static bool initialized = false;
if (!initialized) {
falcon_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
d_ptr->mem_per_token);
initialized = true;
}
return falcon_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
}
int32_t Falcon::contextLength() const
{
return d_ptr->model->hparams.n_ctx;
}
const std::vector<LLModel::Token> &Falcon::endTokens() const
{
static const std::vector<LLModel::Token> out = { 11 };
return out;
}
#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) {
#if 0
uint32_t magic = 0;
f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
uint32_t version = 0;
f.read(reinterpret_cast<char*>(&version), sizeof(version));
if (magic != FALCON_MAGIC) {
return false;
}
falcon_hparams hparams;
f.read(reinterpret_cast<char*>(&hparams), sizeof(hparams));
// we're matching the file format of existing pre-converted models
// compatible with ctransformers llama.cpp based format, which also
// unfortunately shares its magic number what llama uses, so we now
// differentiate by n_vocab
// give some wiggle room over the max to allow for finetunes that expand the
// vocabulary
if (!(hparams.n_vocab >= 65024 && hparams.n_vocab <= 65100)) {
return false;
}
if (hparams.falcon_version != 7) {
return false;
}
return true;
#endif
return false;
}
DLL_EXPORT LLModel *construct() {
return new Falcon;
}
}

42
gpt4all-backend/falcon_impl.h
View File

@ -1,42 +0,0 @@
#ifndef FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#error This file is NOT meant to be included outside of falcon.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#endif
#ifndef FALCON_H
#define FALCON_H
#include <string>
#include <functional>
#include <vector>
#include <memory>
#include "llmodel.h"
struct FalconPrivate;
class Falcon : public LLModel {
public:
Falcon();
~Falcon();
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:
std::unique_ptr<FalconPrivate> d_ptr;
protected:
std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
Token sampleToken(PromptContext &ctx) const override;
std::string tokenToString(Token) 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 // Falcon_H

2
gpt4all-backend/llamamodel.cpp
View File

@ -393,7 +393,7 @@ 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");
isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon");
gguf_free(ctx_gguf);
return isValid;

143
gpt4all-backend/scripts/convert_falcon_hf_to_ggml.py
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@ -1,143 +0,0 @@
# Based on: https://github.com/KerfuffleV2/ggml-falcon/blob/feat-improve-falcon-convert-hf/examples/falcon/convert-hf-to-ggml.py
# Convert Hugging Face fine-tuned bloom-like models to ggml format
#
# Usage:
#
# python3 convert_falcon_hf_to_ggml.py model_directory output_directory [use-f32]
#
# This script is similar to "convert-pt-to-ggml.py"
#
import io
import os
import sys
import struct
import json
import code
import torch
import numpy as np
import gc
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if len(sys.argv) < 3:
print("INFO: GGML V1 files produced are meant to be finalized through examples/falcon_quantize which will bring them to latest version and precision of choice");
print("Usage: python convert_falcon_hf_to_ggml.py model_directory output_directory [use-f32]")
print(" model_directory: name of the directory and model you convert (it should be a subdirectory)")
print(" output-directory: directory where the output file will be written")
print(" use-f32: if present, use float32 instead of float16 (f32 is recommended)")
sys.exit(1)
# num_parts = int(sys.argv[1])
dir_model = sys.argv[1] # name and dir of model
dir_out = sys.argv[2] # output directory
# make sure the output directory exists
os.makedirs(dir_out, exist_ok=True)
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 3:
ftype = 0
tokenizer = AutoTokenizer.from_pretrained(dir_model)
# print(tokenizer)
config = AutoConfig.from_pretrained(dir_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(dir_model, trust_remote_code=True, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
hparams = config.to_dict()
n_head = hparams["n_head"]
n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
head_dim = hparams["hidden_size"] // n_head
print("* Loading model from: ", dir_model)
fname_out = dir_out + f"/ggml-model-{dir_model.split('/')[-1]}-{ftype_str[ftype]}.bin"
fout = open(fname_out, "wb")
fout.write(struct.pack("i", 0x67676a74)) # magic: ggmf in hex (version 1) - possibly change to ggfc ?
fout.write(struct.pack("i", 1)) # version
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["hidden_size"]))
fout.write(struct.pack("i", n_head))
fout.write(struct.pack("i", n_head_kv))
fout.write(struct.pack("i", hparams["n_layer"]))
fout.write(struct.pack("i", 40 if "n_head_kv" in hparams else 7)) # obsolete field that breaks ggml compatibility - todo again remove one day
fout.write(struct.pack("i", ftype))
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
for i in range(hparams["vocab_size"]):
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", 0.0)) # falcon uses bpe on RefinedWeb - no probability scores used
model = model.state_dict()
for name in model.keys():
src = name
# The original query_key_value tensor contains n_head_kv "kv groups",
# each consisting of n_head/n_head_kv query weights followed by one key
# and one value weight (shared by all query heads in the kv group).
# This layout makes it a big pain to work with in GGML.
# So we rearrange them here,, so that we have n_head query weights
# followed by n_head_kv key weights followed by n_head_kv value weights,
# in contiguous fashion.
if "query_key_value" in src:
qkv = model[src].view(
n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
model[src] = torch.cat((q,k,v)).reshape_as(model[src])
data = model[src].squeeze()
n_dims = len(data.shape)
# default type is fp32
ftype_cur = 1 if ftype == 1 and n_dims > 1 else 0
data = data.to(dtype = torch.float16 if ftype_cur == 1 else torch.float32).numpy()
print(f' |', name, data.shape, '->', data.dtype)
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
for i in range(n_dims):
fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
fout.write(str)
# data
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("")